Aluminum bulk micromachining through an anodic oxide mask by electrochemical etching in an acetic acid/perchloric acid solution A well-defined microstructure with microchannels and a microchamber was fabricated on an aluminum plate by four steps of a new aluminum bulk micromachining process: anodizing, laser irradiation, electrochemical etching, and ultrasonication. An aluminum specimen was anodized in an oxalic acid solution to form a porous anodic oxide film. The anodized aluminum specimen was irradiated with a pulsed Nd-YAG laser to locally remove the anodic oxide film, and then the exposed aluminum substrate was selectively dissolved by electrochemical etching in an acetic acid / perchloric acid solution. The anodic oxide film showed good insulating properties as a resist mask during electrochemical etching in the solution. A hemicylindrical microgroove with thin free-standing anodic oxide on the groove was fabricated by electrochemical etching, and the groove showed a smooth surface with a calculated mean roughness of 0.2 -0.3 µm. The free-standing oxides formed by electrochemical etching were easily removed from the specimen by ultrasonication in an ethanol solution. Microchannels 60 µm in diameter and 25 µm in depth connected to a microchamber were successfully fabricated on the aluminum. Bulk micromachining of metals and semiconductors has been widely investigated and developed in the fields of electronic devices, micromachining, and nanotechnology [1] [2] [3] [4] [5] . Aluminum is generally used for circuit materials, cold plates, and heat sinks due to its low electrical resistivity (2.66 x 10 -8 Ωm) and high thermal conductivity (237 Wm -1 K -1 ) [6] , and it is important to develop more precise and smaller bulk micromachining of aluminum for micro-and nano-structure fabrication. The fabrication processes of micro-and nano-meter scale structures are mostly based on photolithography with ultraviolet or X-ray irradiation. However, because photolithography involves many steps with toxic reagents and is seldom applied to materials with three-dimensional structures, a technique for micro-and nano-structure fabrication without photolithography must be developed. Recently the authors have been developed a novel microstructure fabrication for aluminum and its alloy using laser irradiation and electrochemical techniques [7] . In this technique, an aluminum specimen was anodized in sulfuric, oxalic, and phosphoric acid solutions to form a porous or barrier type anodic oxide film. The oxide film was then irradiated with a pulsed neodymium-doped yttrium aluminum garnet (Nd-YAG) laser to remove the anodic oxide, and a patterned anodic oxide film was fabricated on the aluminum substrate. Here, the anodic oxide acted as a patterned resist mask for microstructure fabrication (Fig. 1) . After the laser irradiation, subsequently electro-and electro-less plating caused a selective metal and organic compound deposition on the exposed aluminum substrate, and microstructure fabrication such as micro-coils [8] [9] [10] [11] , printed circuit boards [12] [13] [14] , plastic injection molds [15] , three-dimensional (3D) complicated micromachine components [16, 17] , 3D micro-actuators [18, 19] , and electrochemical micro-reactors [20] , was successfully achieved.. Aluminum bulk micromachining using patterned anodic oxide can also be realized by electrochemical dissolution of the exposed aluminum substrate. However, electrochemical etching of the exposed aluminum in a sodium chloride solution resulted in uneven electrochemical dissolution [21, 22] ; it is difficult to obtain an aluminum microstructure with a flat surface by this process (Fig. 1a) . Therefore, a more suitable electrochemical etching solution should be selected for exposed aluminum bulk micromachining with a flat surface. Here, the authors noted that an acetic acid / perchloric acid mixture solution is generally used for the electropolishing solution of aluminum (Fig. 1b) . It is a well-known experimental fact that anodic polarization of aluminum in solution causes smoothing of microscopic aluminum surfaces with electrochemical dissolution; however, there have been no reports of the details of the dissolution behavior at a selected area in the solution.. In the present investigation, the authors show a new aluminum bulk micromachining process through an anodic oxide mask by electrochemical etching in an acetic acid / perchloric acid solution. The goal of the current investigation is to describe the optimal conditions for the laser irradiation and electrochemical etching processes and to form well-defined microchannels with flat surfaces on an aluminum substrate by the developed bulk micromachining.. 2. Experimental 2.1 Specimens and pretreatment Fig. 2 shows a schematic model of the aluminum bulk micromachining through an anodic oxide mask by electrochemical etching in a CH 3 COOH / HClO 4 solution. The fabrication process consisted of the following four steps: a) aluminum anodizing, b) laser irradiation to remove the anodic oxide, c) electrochemical etching at the laser-irradiated area, and d) ultrasonication to remove free-standing oxides. Highly pure aluminum plates (99.99 wt%, 400 µm thick, Nippon Light Metal Co., Japan) were used as the starting specimens for microstructure fabrication. The specimens were cut into 30 mm x 20 mm pieces with a handle and then ultrasonically degreased in an ethanol solution for 10 min. After degreasing, the specimens were electropolished in a 13.6 M CH 3 COOH / 2.56 M HClO 4 solution (T = 280 K) with a constant voltage of 28 V for 150 s. The electropolished specimens were anodized in a 0.22 M (COOH) 2 solution (T = 293 K) for 3 min with a constant current density of 100 A m -2 to form a 1 µm thick porous anodic oxide film on the aluminum substrate (Fig. 2a) . A platinum plate was used as the counter electrode, and the solution was stirred with a magnetic mixer during anodizing. This investigation demonstrated a new aluminum bulk micromachining process by anodizing, laser irradiation, and electrochemical etching in an acetic acid / perchloric acid solution, and the following conclusions may be drawn from the investigation.. Electrochemical etching in an acetic acid / perchloric acid solution through an anodic oxide mask enables the fabrication of microgrooves with free-standing oxides on an aluminum substrate, and the width and depth of the grooves are determined by the electrochemical etching time. During electrochemical etching, the anodic oxide film formed by anodizing has good insulating properties in the solution. The free-standing oxides by the etching are easily removed from the specimen by ultrasonication. A microstructure consisting of microchannels 60 µm in diameter and 25 µm in depth and connected with a microchamber can be successfully fabricated by aluminum bulk micromachining.

B B I O O O O O O B I O O O O O O O O O O O O O O O O O O B O O O O O O O B B I O B B I B I O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O B I O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B I O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B I O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B I O O O O O O O O O O O O O O B O O B I O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B I O O O O O O B B I O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B I O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O B I O B I O O O O O O O O O O O B O O O O B I O O O O O O O O O O O O O O B B I O O O O O O B I O O O O O O O O O O O O O O O O O O O B B O B I O O O O O O B I O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O B B I O O B B I O B I O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B I O O B I O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B I


CONCOR: context-aware community-oriented routing for intermittently connected network Delay-Tolerant Networks (DTNs) are characterized by time-varying and partially connected network topology. In such networks, mobility of node may be by virtue of its carrier. Social beings such as humans may act as a carrier of mobile nodes, and therefore mobility pattern of node follows a social-based movement model. Due to intermittent connectivity, end-to-end path between source and destination is rare and therefore message delivery is a challenge in such networks. In this paper, we present a new routing approach, context-aware community-oriented routing (CONCOR) that exploits the community and the context awareness of nodes, for efficient message delivery. Nodes with a common point of interest form a dynamic community. We have identified a set of node's attribute (context) and formulated a utility function to determine its capability to deliver a message. CONCOR is a multihop routing approach exploiting the nodes as message relays (store-carry-forward). Through simulation, we validated the effectiveness of node's attributes in estimation of message delivery probability. The simulations were done for our approach using the social movement-based data set on Opportunistic Network Environment (ONE) simulator. We compared our approach with dynamic social grouping (DSG)-based routing and context-aware routing(CAR) on three metrics viz. message delivery ratio, message traffic ratio and average message delay. We found that the results of our approach outperforms the DSG and CAR on all the three metrics. A Delay-Tolerant Network is an occasionally connected network that may experience frequent, long-duration partitioning and may never have an end-to-end contemporaneous path.. Research in the area of Delay-Tolerant Networks (DTNs) has been receiving considerable attention in the last few years owing to their widespread occurrence in a variety of applications such as : a) InterPlaNetary(IPN) internet project [1] , b) Wizzy digital courier service which provides asynchronous (disconnected) internet access to schools in remote villages of South Africa [2] , c) transmission of information/message during mission critical operations like natural disasters or battle zones.. One of the earliest approach proposed for routing in partitioned networks is epidemic routing [3] . However, it is very expensive in terms of message traffic and buffer *Correspondence: rahul@ipu.ac.in 1 USICT, Guru Gobind Singh Indraprastha University, Sector 16C, Dwarka, Delhi 110078, India Full list of author information is available at the end of the article space, which reduces the life of the network. To overcome the problem of heavy traffic and high buffer requirement, probabilistic routing [4, 5] and social routing [6] [7] [8] [9] [10] schemes were proposed. Both the approaches provide considerable improvement in the delivery ratio under low buffer requirement.. The probabilistic routing schemes are based on the heuristic that if a node has interacted with a group of nodes in the past, it will do so again in the future. On the other hand, social routing assumes that the mobility of node is due to the mobility of its carrier (social beings, mostly humans). Therefore, movement of node follows a pattern known as social-based movement pattern. In such movement model, contacts established between nodes are not random; rather, they are based on the interest of social beings. Both probabilistic and social routing have two basic limitations. Firstly, the two approaches exploit only opportunistic contact wherein a message is forwarded by a node, say A, to the first node, say B, encountered with higher delivery probability than A. This is true, even if a node, say C, with higher message delivery probability than B is available in the neighbour of A. Secondly, their performance in terms of message delivery ratio heavily depends upon the function chosen to compute the message delivery probability of nodes. In some of the routing approaches [8, 11, 12] , delivery probability of node is computed using multiple attributes of node such as buffer space, battery level, colocation of nodes etc. Values to these multiple attributes is provided by context awareness of nodes.. Two categories of routing schemes have been proposed for DTN. First one exploits the opportunistic contact, and the other one uses the source based routing. For both the approaches, decision to select next hop node is to be made. For opportunistic contact, a decision is to be made whether to forward or not to forward a message to an encountered node. Whereas, in case of source-based routing, a decision is to be made, to which neighbouring node the message should be forwarded. This decision depends upon a node's capability (also called delivery probability) to deliver a message to destination. The routing schemes proposed in literature [4, 13] suggest various node attributes for determining its delivery probability.. In this work, we present a routing approach, contextaware community-oriented routing (CONCOR) , that exploits the existence of social groups [10] along with the context awareness of nodes for efficient routing of the messages in DTN. The context [11] has been defined as set of attributes that represent various characteristics of a system, such as available buffer space, maximum buffer space, information about node's community etc. A node may exploit the context information for efficient message delivery. The social groups known as community are formed around immobile base stations located at point of interest (POI) of social nodes. Each of the base station represents a distinct community, and initially it is the only member of its community. When a node frequently comes in contact with a base station and its contact strength [13] increases beyond certain threshold, it becomes a member of a community. We have assumed that members of community are disjoint. As communities are built around the base stations therefore number of communities in a network is equal to number of base stations. We assume that: n = number of base stations N = number of mobile nodes. The number of members in each community may vary. For context awareness, multiple attributes of node, such as buffer space, community, battery level etc., have been identified. Using the identified attributes, the message delivery probability of a node is computed with respect to each base station. We assume that base station serves as a sink for messages and nodes are the source and relays for messages. For routing a message, each node maintains a delivery probability vector (DPV) table. Each record of DPV table is timestamped as detailed in Section 4.5 and represents a delivery probability vector (DPV) received from neighbouring nodes. The timestamp of a record remains valid for a limited time beyond which the DPV of a node becomes invalid and cannot be used for routing. When a node has a message to be delivered to a base station, it searches, in its DPV table, for the best possible neighbouring node whose message delivery probability for a base station is higher than itself. If such a node is found, the node forwards the message. Using simulation, we validate the effectiveness of each of the attribute in estimating the message delivery probability of node.. We have used multiple factors for computing a delivery probability vector. In [11] , node's mobility, i.e. number of different connections established per unit time, is used as one of the factors for determining node's probability to deliver a message. We have shown that large number of different connections established by a node per unit time may not add to node's capability to deliver a message. Through simulation, we show that our approach outperforms both DSG (routing approach using dynamic social grouping that exploits opportunistic contacts) and CAR (Context-Aware Routing using source-based routing approach) in terms of message delivery ratio, average delay per delivered message and average message traffic per delivered message.. Although, in this paper, we have assumed the nodes to be collaborative and participating in network activities, but the impact of selfish behaviour of nodes on network performance cannot be denied specially if only a single copy of a message is maintained as it happens in first contact routing. In our approach, as multiple copies of a message are forwarded through different routes, therefore the impact of few nodes showing selfish behaviour will be small. As the study of impact of selfish behaviour of nodes on network performance is outside the scope of the paper, we would like to mention some of the studies conducted by other researchers about selfish behaviour of nodes. In [14] , authors have studied the impact of distribution of selfish behaviour on performance of mobile social networks. Their work supports our view that existence of multiple paths makes social networks more robust to selfish behaviour of nodes.. The paper is organized as follows: Section 2 describes the problem. Section 3 presents the related work done in the area of routing in Delay-Tolerant Network. Section 4 presents our approach. Section 5 presents the CONCOR algorithm. Section 6 presents the experimental setup and analysis of the results generated after the implementation of the algorithm on Opportunistic Network Environment (ONE) simulator [15] . Section 7 presents the conclusion and future work. Figure 1 represents a scenario of a university system spanned over a large hilly area, with number of schools (departments) such as computer science, electronics, arts, commerce, biotechnology, administration, finance etc. The hexagons in Fig. 1 represents physical boundary of department. Each hexagon has one base station whose transmission range is shown by a dotted circle. The location of base station represents the office of head of department. The circles, triangles, rectangles and squares represent people working in different departments. People working for a department will come in frequent contact with the base station of their department. The office of the head of a department represents a point of interest (POI) for people working for that department. People working in a university may carry a communicating device such as iphone or ipad etc. The mobility of the device is guided by people movement. Over a period, the communication devices will come in contact with base station and will form a community around base station. Intuitively, nodes belonging to one department will form a community with the base station of that department.. Due to lack of wired infrastructure and hilly terrain, the existence of connected end-to-end path between the source (mobile devices) and destination (base station) of message is rare. In this scenario, routing a message from source to destination is a challenge. Using store-carryforward approach to deliver a message is a solution, but delays are inevitable. In the absence of complete route from source to destination, choosing a next hop node is a decision problem. The proposed algorithm exploits the existence of community in social networks. It also makes use of context awareness of nodes to improve the estimate of message delivery probability of a node. The simulation results shows an improvement of message delivery ratio, message traffic ratio, and reduces delay significantly over both DSG and CAR. The work also highlights the effectiveness of node's attribute at estimating its message delivery probability. Further, as future work, an application of CONCOR may be widened to a network with N message source and sink. Also, the performance of the CONCOR will be studied through its deployment in the real-time scenario where a node's message delivery capabilities are likely to get affected by geographical terrain and environmental interferences. One of the earliest approaches proposed for routing in partitioned networks is epidemic routing [3] . The goal of epidemic routing was to maximize the message delivery ratio while minimizing the message delivery time. However, it is very expensive in terms of message traffic and buffer space, which reduces the life of the network. That is, the approach does not seem to scale as the number of messages in the network grows. An intelligent buffer management scheme can improve the delivery ratio over the simple FIFO scheme.. Spray and Wait [16] routing scheme was proposed to provide controlled forwarding thus leading to reduced message traffic.. To enhance the performance of routing schemes, several researchers studied the characteristics of relay node to compute its delivery probability. It was observed by researchers [4, [17] [18] [19] that the mobility is mostly not random and there is a pattern in encounters. The probabilistic routing scheme is based on the idea that nodes that have met in the past will again do so in the future and therefore nodes that have met the destination directly or through transitivity will have higher probability of delivering a message.. The routing problem in wireless networks (with intermittent connectivity) is transformed into a decision problem wherein a node having a message has to choose the next best hop (relay) node for message forwarding instead of relying on opportunistic contact. Various routing schemes for DTN evaluate the message delivery capability of a relay node for message forwarding.. Delivery probabilities are computed using the history of encounters and transitivity to reflect that if a node has been encountered in the past, it is likely to be encountered again. The approach introduced by Wang et al. in [5] is also based on probabilities of nodes of successful delivery of a message to a base station. However, the delivery probabilities are computed based on successful delivery of messages rather than regular contacts between the nodes.. As the movement of nodes is mostly not random, it was also observed that it is affected by the interest of its carrier, mostly social being. Various routing schemes exploited the social-based movement patterns for choosing the most appropriate relay node.. The SimBet routing algorithm [6] borrows ideas from social networking and contact patterns to predict paths to destinations for improving delivery ratio and time. The algorithm assumes that the groups have been formed and calculates the betweenness and centrality metrics to make routing decisions. Bubble rap [7] extends their work by allocating nodes into social groups based on direct and indirect contacts. Based on the global knowledge of the nodes' contact, they construct k-cliques/clusters to define the groups. However, grouping is static in their approach.. In [13, 20] , authors presented an approach that combines the advantage of social grouping and message forwarding based on message delivery probability of a node. Groups are formed dynamically as nodes come in contact with each other. The message is either forwarded using the individual delivery probability of a node, which is computed on the basis of messages forwarded by a node or by using the group probabilities of all those groups of which node is a member. In [13] , the authors present an application of social grouping on network with single base station; and in [20] , approach for network with n message source and sink is presented. The authors have presented an approach for identification of social groups dynamically. The approach suffers from overhead involved in creation and maintenance of groups.. To better exploit the social-based movement for message forwarding, the characteristics of nodes movement were studied. Working day movement model was proposed in [21] .. In [22] , authors presented the impact of movement characteristics on contact and inter-contact time. The contact time was used for determining the utility of contact being established. By capturing these characteristics of movement, the authors proposed prediction of the activities being performed by a node carrier.. Nodes capability as a relay node not only depends upon the movement pattern of its carrier but it also depends upon other attributes. In [23] , routing scheme has been proposed that computes message delivery probability of a node on the basis of buffer space.. In [11] , authors present an idea of CAR (Context-Aware Routing) protocol for routing of the message in DTN. The protocol is based on the idea of exploiting nodes as carriers of messages in a partitioned network to achieve message delivery. The choice of the best carrier is made using Kalman-filter-based prediction techniques and utility theory. The authors used two of the node's attribute viz-a-viz, colocation and change degree of connections, for estimating the delivery probability of a node. In the said approach, a single copy of message is forwarded. The approach does not exploit the social contact pattern of nodes.. In [24] , the authors proposed a distributed scheme for sparse networks by calculating resources such as buffer, bandwidth and energy levels to take crucial routing decision. The routing decision is based on the quantum of resources available in a node's vicinity. However, authors does not exploit the existence of communities for efficient routing decisions.. In [25] , authors proposed a framework for energyefficient message forwarding using opportunistic contacts. In their work, they have proposed static and dynamic forwarding policies. Unlike the probability of forwarding a message by a node, as used in [25] , in this paper, we discuss the message delivery probability of node. As per our approach, a node forwards a message only when it encounters a node that is more capable of delivering a message to its destination. This any how makes efficient use of energy by controlling the message forwarding and at the same time reduces message traffic.. In [26] , authors proposed the usage of throwboxes, battery power stationary nodes, for enhancing the performance of DTN. In [27, 28] , authors proposed further enhancement in the architecture of throwboxes so as to make them energy efficient. The authors proposed a multitiered, multiradio hardware platform that applies heuristic for using the two tiers. By using low-power tier for listening the beacons and high-power tier for message exchange, throwboxes are able to provide energy-efficient solutions to DTN in general and sensor networks in particular. The proposed architecture can be implemented in DTN nodes, and its effect on the performance may be studied further. However, the implementation is outside the scope of this paper.. The knowledge of nodes' physical location and a schedule of futuristics locations or contacts have also been utilized for routing in DTN. In [2] , authors have proposed an approach for routing in DTN using modified Dijkstra algorithm. They assume that the DTN nodes possess the knowledge about the time and duration of contact. On the basis of this knowledge, a node may create a number of routing metrics. Their results show that the efficiency and performance increases with the amount of information used for the metric. In DTN, determining the futuristic knowledge of opportunistic contacts is extremely difficult and therefore the approach presented in [2] may only exploit the scheduled contacts for message transfer and opportunistic contact may not be used. The weakness of this approach is that each node must have access to updated schedule data.. In [29] , authors have presented an approach that uses link state routing protocol. The link state packets are exchanged using epidemic approach which leads to overhead. Whenever a node has a message to transfer, it forwards message to the node which is closest to the message destination. The approach is good in the sense that it requires very small buffer space and is therefore scalable. But the approach may not be practical in the scenario wherein the nodes have sparse connectivity. The Opportunistic Network Environment (ONE) simulator implemented in Java and available as open source has been used to evaluate the algorithm. The ONE simulating environment is capable of simulating mobility patterns of nodes and message exchange between them. Many of the routing algorithm applicable to DTN environment are pre-implemented in the simulator. We implemented routing as used in our algorithm (CONCOR), the DSG-based routing algorithm [13] and context-aware routing [11] . Three metrics viz. message delivery ratio, message traffic ratio and delay per delivered message were used to compare the performance of DSG, CAR and CONCOR. The simulator generated total messages delivered, message delivery ratio, total and average message traffic per delivered message. The simulator was customized to generate total delay and average delay per delivered message. The simulation was run for non-replicated messages.

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O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Synergistic effect of different levels of genomic data for cancer clinical outcome prediction There have been many attempts in cancer clinical-type classification by using a dataset from a number of molecular layers of biological system. Despite these efforts, however, it still remains difficult to elucidate the cancer phenotypes because the cancer genome is neither simple nor independent but rather complicated and dysregulated by multiple molecular mechanisms. Recently, heterogeneous types of data, generated from all molecular levels of 'omic' dimensions from genome to phenome, for instance, copy number variants at the genome level, DNA methylation at the epigenome level, and gene expression and microRNA at the transcriptome level, have become available. In this paper, we propose an integrated framework that uses multi-level genomic data for prediction of clinical outcomes in brain cancer (glioblastoma multiforme, GBM) and ovarian cancer (serous cystadenocarcinoma, OV). From empirical comparison results on individual genomic data, we provide some preliminary insights about which level of data is more informative to a given clinical-type classification problem and justify these perceptions with the corresponding biological implications for each type of cancer. For GBM, all clinical outcomes had a better the area under the curve (AUC) of receiver operating characteristic when integrating multi-layers of genomic data, 0.876 for survival to 0.832 for recurrence. Moreover, the better AUCs were achieved from the integration approach for all clinical outcomes in OV as well, ranging from 0.787 to 0.893. We found that the opportunity for success in prediction of clinical outcomes in cancer was increased when the prediction was based on the integration of multi-layers of genomic data. This study is expecting to improve comprehension of the molecular pathogenesis and underlying biology of both cancer types. An understanding of the molecular basis of cancer brings many benefits for predicting clinical outcomes of cancer and for determining the corresponding best treatment. Since cancer is related to alterations in the genes that control normal cell growth and death, molecular-based diagnostics are promising in that they may provide more opportunities for objective, precise, and systematic predictions on cancer. Data at the multiple molecular levels, generated from all levels of 'omic' dimensions from genome to phenome ( Fig. 1) , have recently become more available. At the genome level, copy number variants have attracted considerable attentions, since alterations of genomic DNA can be explored by expanding the scope of view to a larger region of the genome or to chromosomes. At the epigenome level, data from DNA methylation, which plays a crucial role in the control of gene activity, is of interest, while at the level of the transcriptome, gene expression and microRNA (miRNA) are the most representative datasets. DNA microarrays have already been widely used for the classification of tumor subtypes or clinical outcomes for the diagnosis, treatment, or prognosis of cancer for many years [3, 13, 15, 19, 34, 47] . More recently, miRNA has become available for understanding the inhibition of expression on target mRNAs in gene regulatory networks.. There have been attempts at cancer classification using a set of miRNA, copy number alterations (CNAs), and DNA methylation [4, 6, 27, 30, 51] . Despite these efforts, however, it still remains difficult to elucidate the cancer phenotypes because the cancer genome is neither simple nor independent but rather complicated and dysregulated by multiple molecular mechanisms [9, 18] . For example, the cancer genome is related to mutations in coding and non-coding sequences, changes in the DNA structure and copy number, DNA methylation and histone modification, and miRNA regulation. Those possibilities lead to many alternative forms of cause-and-result in transcription, translation, post-translational modification, and eventually, gene and protein functions (Fig. 1 ). Thus, no single level of genomic data will be sufficient to comprise all of the information in the mechanism, and hence, a consideration of the layered process of biological systems through incorporation of multiple levels of genomic data will provide a much more reasonable prediction of cancer phenotypes.. The Cancer Genome Atlas (TCGA) is a collaborative initiative to improve understanding of cancer using existing large-scale wholegenome technologies. The TCGA research network lately published two notable papers on glioblastoma patients concerning an interim analysis of DNA sequencing, copy number, gene expression, and DNA methylation data [45] , and discovery of links between cancer subtypes and different neural lineages with gene expression [48] . While the TCGA opens many opportunities to researchers to deepen the knowledge of the molecular basis of cancer [16, 24] , it is particularly important to access multiple data sources as we propose here.. In this paper, we propose an integrated framework that uses multi-level genomic data sources for the molecular-based classification of clinical outcomes in brain cancer (glioblastoma multiforme; GBM) and ovarian cancer (serous cystadenocarcinoma; OV). GBM is the most common and aggressive primary brain tumor in adults [14] , and notorious for its tendency to recur [37] . Despite recent advances in the molecular pathology of GBM, the underling molecular mechanisms associated with clinical outcome are still poorly understood [14, 38] . OV is one of the most common gynecological malignancies, and is the 5th leading cause of cancer mortality in women in the United States [21] . Understanding the molecular pathogenesis and underlying biology for both types of cancer is expected to provide guidance for improved prognostic indicators and effective therapies.. In computational biology, this work will be a pioneering attempt to predict the cancer phenotype based on the underlying complex biological mechanisms. From individual TCGA data sources, we conducted empirical comparisons at each level of genomic data; to deduce possible biological implications based on the results of the relative contribution of each piece of data to increase prediction accuracy. We show that accuracy of prediction increases because of incorporation of information fused over heterogeneous biological data sources, providing an enhanced global view on cancer mechanisms.. Several approaches to multiple data integration have been applied to protein function prediction such as the kernel-based integration framework [2, 22, 33] , the Relevance Vector Machine (RVM) approach [52] , and a Bayesian model [20] . In recent years, we developed an integration method of protein networks based on graph-based semi supervised learning (SSL) [41, 46] . SSL is a halfway method between unsupervised learning and supervised learning, which takes an advantage of both unlabeled data and labeled data. Here, we focus on graph-based SSL due to their solid mathematical background, model visualization, sparseness properties as well as the close relationship with kernel methods. In terms of integration issues, learning with other methods such as support vector machine (SVM) and Markov random field (MRF) may not be finished in a reasonable time when the data is large-scale, e.g. TCGA dataset (Appendix A). Although, any of the above mentioned methods could be used to implement the proposed idea, the graphbased SSL is employed in this study, taking advantage of computational efficiency and representational ease for the biological system. The learning time of graph-based SSL is nearly linear with the number of graph edges, which in most biological networks is few, while the accuracy remains comparable to the kernel-based methods that suffer from the relative disadvantage of a longer learning time [42, 46] . In addition, the interpretation of biological phenomena can be improved because of the graph data structure [31, 40, 44] , which naturally fits into the graph based SSL.. There have been several studies for integrative analysis of different levels of genomic data such as pairs of 'CNA-gene expression' [7, 29, 39] , 'miRNA-gene expression' [23, 32, 50] , and 'methylationgene expression' [12, 25] . These approaches are generally based on regression or correlation analysis, which fit into combining two levels of genomic data and are not designed to accommodate another level of genomic data. Thus, these methods have a difficulty to integrate more than three types of genomic data because of non-scalability. On the other hand, many integrative approaches have been proposed to combine more than three types of genomic data through the gene-based integration; that is, heterogeneous genomic data from different sources were analyzed sequentially, then each genomic data mapped into the gene level for the integration [26, 35, 36] . However, information loss might occur by such gene-based integration from the different levels of genomic data because there is no guarantee to be a simple one-to-one relationship between each feature of multi-layers of genomic data and a specific gene such as relations of exon-gene, miRNA-gene, CNAgene, and methylation-gene. Therefore, here we propose a data integration framework for different levels of genomic data, not only with a scalable formalism to extend another level of genomic data but preserving level-specific properties from the multi-layers and heterogeneous genomic data.. The manuscript is organized as follows. Data description and methods for graph-based SSL and the integration approach are explained in Section 2. In Section 3, experimental results in GBM and OV are provided to demonstrate the validity and effectiveness of our integrative approach. Finally, we discuss the meaning of our study and future works in the last section. Since cancer is the phenotypic end-point of events cumulated through multiple levels of the biological system from genome to proteome, a single layer of biological information will not be sufficient to fully understand tumor behavior or the underlying biological mechanisms [18] . In the present study, a pilot framework of integration of multiple levels of genomic data sources, CNA, DNA methylation, gene expression, and miRNA expression, has been applied to the problem of prediction of clinical outcomes in GBM and OV.. When comparing individual genomic data sources, we suggested some preliminary insights that medical experts or biologists may consider. The main result of our study is that integration of multi-layers of genomic data sources increases the opportunity of success in prediction of clinical outcomes in cancer. To the best of our knowledge, this study is the first effort in molecular-based classification of clinical outcomes from cancer patients, by unfolding and integrating the genomic, the epigenomic, and the transcriptomic features in their samples. In addition, the proposed framework would be easily extended when novel genomic data from different levels is available.. This study underpins our on-going work. There are possible relationships between the sample features (attributes) belonging to different layers of genomic data such as 'miRNA-target genes,' 'copy number alteration region-genes located in the alteration region,' and 'DNA methylation site-specific genes regulated by promoter regions.' Therefore, when integrating multiple genomic data, it will be desirable that a framework will be capable of containing the inter-relationships between sample features belonging to different layers of the biological system.. Recently, there has been an announcement from TCGA that additional cancer genomic data for about 20-25 tumor types will be generated in the next few years as the second phase of the project. With abundance in multi-layers of genomic and clinical data, our proposed integrative framework will be valuable for elucidating the underlying tumor behavior, eventually leading to more effective screening strategies and therapeutic targets in many types of cancer. The Matlab code will be available upon request. From the multi-levels of genomic data available from TCGA, the following five sets of clinical outcome classifications were defined: for GBM, (1) short-term or long-term survival and (2) initial or recurrent tumor and for OV (3) early or late stage; (4) low or high grade; (5) short-term or long-term survival. For the five sets of binary classification problems, the proposed approach, prediction from integration of multi-level genomic data sources, was compared with the four individual predictions obtained from CNA, methylation, gene expression, and miRNA. For each problem, we calculated the area under the curve (AUC) of receiver operating characteristic (ROC) [17] and the true positive rate yielding an 1% false positive rate (TP1FP) as performance measurements [22] . The dataset was randomly splited so that 80% of the samples are assigned to the training set and 20% to the validation set to find the model parameters. The overall performance was measured through three times of fivefold cross validation. The Wilcoxon signed-rank test was used to validate the significance of the difference in performance for all the combinations of the comparisons [11] . Details on the model parameter selection are available in Supplementary methods. Table 3 shows the AUC performance on the two sets of classifications of GBM clinical outcomes. The AUCs of the four individual sources (CNA, methylation, gene expression, and miRNA) are shown in the first four rows and the AUC of the proposed approach-integration with multi-level genomic data sources is shown in the fifth row. For the short-term survival vs. long-term survival classification, the SSL with gene expression data performed best with 0.8560 AUC (underlined) among the four genomic data sources, and CNA data showed comparable performance with an AUC of 0.8160. However, none of the AUC values from single data sources could outperform the AUC of 0.8760 (boldface) generated by the multi-level genomic data. The p-values of the pairwise comparisons in AUC between the proposed approach and the single data approaches demonstrate that a statistically significant difference exists in performance. The superior performance of the integration approach is also found in terms of the value of the TP1FP-of 0.8, the highest among the five. Furthermore, for the initial tumor vs. recurrent tumor classification, the SSL with CNA data showed the best performance with an AUC value of 0.8131 (underlined) among the four individual data sources, but again the performance of the integration approach was superior to that of the best individual approach with an AUC of 0.8369 and TP1FP of 0.75 (boldface) was superior to all of the individual approaches. Table 4 shows the AUC performance of the three sets of classification problems of OV clinical outcomes. For the short-term survival vs. long-term survival classification, the SSL with gene expression data performed best with an AUC of 0.7651 when compared with the other single genomic data sources. Note that a similar result was obtained for GBM, which might imply that the information from gene expression data plays a critical role in the classification of short-term vs. long-term survival. Among the four individual sources, the best performing data differed for each classification; for the early stage vs. late stage classification, the prediction from CNA data showed the best performance (0.8767 AUC) while for the low grade vs. high grade classification, methylation data performed best (0.8161 AUC). However, the AUC of the proposed approach consistently outperforms the individual best for all of the three sets of problems, attaining values of 0.7867, 0.8932, and 0.8678. There is a slight degradation of the proposed approach in TP1FP for the low grade vs. high grade classification, but the magnitude in difference is negligible (0.57 in methylation data vs. 0.54 in multi-level integrated data).

O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Ranking

MEDLINE

documents

Background:

BioTextRetriever

is

a

Web-based

search

tool

for

retrieving

relevant

literature

in

Molecular

Biology

and

related

domains

from

MEDLINE.

The

core

of

BioTextRetriever

is

the

dynamic

construction

of

a

classifier

capable

of

selecting

relevant

papers

among

the

whole

MEDLINE

bibliographic

database.

"Relevant"

papers,

in

this

context,

means

papers

related

to

a

set

of

DNA

or

protein

sequences

provided

as

input

to

the

tool

by

the

user.

Methods:

Since

the

number

of

retrieved

papers

may

be

very

large,

BioTextRetriever

uses

a

novel

ranking

algorithm

to

retrieve

the

most

relevant

papers

first.

We

have

developed

a

new

methodology

that

enables

the

automation

of

the

assessment

process

based

on

a

multi-criteria

ranking

function.

This

function

combines

six

factors:

MeSH

terms,

paper's

number

of

citations,

author's

h-index,

journals

impact

factor,

author

number

of

publications

and

journal

similarity

function.The

best

results

highlight

the

number

of

citations

and

the

h-index

factors.We

have

developed

and

a

multi-criteria

ranking

function,

that

contemplates

six

factors,

and

that

seems

appropriate

to

retrieve

relevant

papers

out

of

a

huge

repository

such

as

MEDLINE.

We

have

developed

a

new

methodology

based

on

Machine

Learning

techniques

to

construct

a

classifier

in

real

time

for

classifying

MEDLINE

papers.

We

have

devised

and

assessed

several

ways

of

partitioning

the

data

and

combining

the

Machine

Learning

algorithms

in

order

to

achieve

a

good

performance

in

the

classification

process.

From

this

study

we

were

able

to

conclude

that

the

best

Machine

Learning

algorithms

to

achieve

a

good

performance

are

the

Ensemble

of

Classifiers

(a

method

that

combines

the

individual

decisions

of

a

set

of

classifiers

through

majority

or

voting).

In

terms

of

the

accuracy

of

the

results,

the

Ensemble

of

algorithms

achieved

an

accuracy

of

and

the

stand

alone

classifiers

achieved

an

accuracy

of

The

results

show

that

the

use

of

Machine

Learning

is

extremely

valuable

to

automate

the

Information

Retrieval

process

with

good

performance

results..

In

this

paper

we

have

proposed

a

new

methodology

that

enables

the

automation

of

the

assessment

process

of

a

multi-criteria

ranking

function..

BioTextRetriever's

last

procedure

is

to

organize

the

papers

selected

as

relevant

by

the

classifier.

In

fact,

this

set

of

papers

classified

as

relevant

is

quite

large

and

it

is

not

advisable

to

present

such

a

huge

number

of

papers

to

the

user.

We

proposed

an

integrated

ranking

function

that

combines

MeSH

terms,

PubMed

number

of

citations,

author

PubMed

h-index,

journals

impact

factor,

authors

number

of

PubMed

publications

and

journal

similarity

factor

i

Since

we

do

not

have

access

to

an

expert

to

evaluate

the

results

of

the

ranking

function,

we

have

adopted

a

procedure

where

the

relevant

papers

associated

to

the

original

sequences

are

the

ones

that

maximize

the

presented

ranking

function

if

they

appear

in

the

first

results.

Since

these

papers

are

guaranteed

to

be

relevant

(because

they

are

associated

to

the

original

sequences)

we

use

them

as

an

alternative

to

the

fact

that

we

do

not

have

access

to

an

expert.

The

ranking

function

is

evaluated

by

analyzing

the

first

papers

that

are

presented

to

the

user

in

descendant

order

of

relevance

by

the

ranking

function,

and

counting

the

number

of

papers

from

the

relevant

papers

associated

with

the

introduced

sequences

that

maximize

the

ranking

function.

The

best

combinations

maximize

the

number

of

citations

and

the

h-index.

BioTextRetriever

was

configured,

by

default,

with

this

coefficients

combination,

however

the

user

can

introduce

other

weights

for

each

factor.

It

is

very

important

for

researchers

to

be

aware

of

the

relevant

scientific

research

in

their

scientific

area

of

knowledge.

However

the

volume

of

scientic

and

technical

publications

in

almost

all

areas

of

knowledge

is

growing

at

a

phenomenal

rate.

Most

of

these

publications

are

available

on

the

Web.

Thus,

accessing

the

right

and

relevant

information

amidst

this

overwhelming

amount

of

information

available

in

the

Web

is

indeed

of

great

importance,

albeit

difficult

in

most

cases

When

trying

to

find

relevant

publications,

researchers

turn

to

the

well

known

traditional

keyword-based

search

engines,

which

returns,

as

a

result,

a

huge

list

of

publications,

that

usually

include

a

large

number

of

irrelevant

ones

To

tackle

this

problem,

research

in

Text

Mining

and

Information

Retrieval

has

been

applied

to

literature

mining

in

order

to

help

researchers

to

identify

the

most

relevant

publications

We

have

developed

a

Web-based

search

tool,

BioTextRetriever,

to

find

relevant

literature

associated

with

a

set

of

genomic

or

proteomic

sequences.

BioTextRetriever

uses

Text

Mining

and

Machine

learning

techniques.

Machine

Learning

techniques

are

used

to

automatically

train

a

classier

that

learns

with

the

papers

associated

with

each

set

of

input

sequences.

The

learned

classifier

is

then

used

in

the

process

of

retrieving

relevant

papers

from

a

larger

repository

such

as

MEDLINE

a

BioTextRetriever

also

organizes

the

papers

selected

as

relevant

by

the

classifier

using

a

ranking

function.

We

have

proposed

and

evaluated

a

ranking

function

that

combines

MeSH

terms

b

paper's

number

of

citations,

author's

h-index,

journals

impact

factor,

authors

number

of

publications

and

journal

similarity

factor

c

In

the

rest

of

this

article

we

first

present

the

related

work

(Section

"State-of-the-art").

In

Section

"BioTextRetriever

architecture"

we

describe

the

system's

architecture.

The

methodology

used

for

the

automatic

classifier

construction

process

is

described

in

the

Section

"Methods".

The

ranking

process

is

presented

in

Section

"The

ranking

process".

The

proposed

ranking

function

is

explained

in

detail

in

Section

"The

ranking

function".

The

experimental

evaluation

of

the

ranking

function

is

described

in

Section

"Choosing

the

ranking

function

coefficients".

We

discuss

the

results

of

such

experiments

in

Section

"Results

and

discussion".

Finally

we

draw

the

conclusions

in

Section

"Conclusions".

In

this

section

we

describe

the

methodology

we

used

for

designing

BioTextRetriever.

B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O B O O O O O O O O B O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Information management in distributed collaborative systems: The case of collaboration studio This paper presents the Collaboration Studio (CS) system, its argumentation and data-structuring models and gives some insights for dealing with information divergence. The system allows discussions among a group of participants that includes a coordinator. The working mechanisms implemented within CS are perfectly transparent to the user, hiding implementation details, giving an appealing and user-friendly environment, and so users do not have to worry about patterns of data distribution, or the details of distribution management. CS shares characteristics with other collaboration computational tools, such as synchronous and asynchronous support and both group working spaces and a local working space. However, its main purpose differs in that, instead of trying to achieve a single document as the outcome of the joint work of several users, CS aims to achieve a broader objective, which is to register (and to demonstrate) the ''path'' used to obtain certain knowledge. Information systems and information technology are leading to a global organizational change. Some of the more dramatic examples are those where entrepreneurs have created radical new business structures supported by the power of information technology and the arrival of new networked or virtual organizations (Boddy et al., 2002, p. 167) . Information becomes the organizationÕs main power source, its main fuel and most important resource or input (Chiavenato, 2000) , and the knowledge workers (Drucker, 1988) gain vital importance, as they constitute a scarce resource in the economy, moving freely within and between organizations, presenting new challenges, such as knowledge management and organizational memory (Conklin, 2001 ). However, it is known that organization knowledge does not lie within some sort of technological information storage, the importance of humans in the process is fundamental. Nevertheless, they cannot be simply detached. Technological artefacts (such as collaborative systems, databases, knowledge bases, etc.) can be useful for capturing explicit knowledge (for more details on explicit and tacit knowledge see Nonaka and Takeuchi, 1995) and maintaining it in proper repositories, which should be available to all the appropriate people in an organization (Courtney et al., 2000) . The major drawback is that less expensive data storage mechanisms in conjunction with increased processing capabilities and networking can generate information overload (Nunamaker et al., 2001) , hampering information retrieval appropriate to teamsÕ needs. Mere file or document storage (usually known as formal knowledge objects, Conklin, 2001 ) becomes inefficient. These records are kept in filing cabinets, folders, in the memory of personal computers, on computer discs and in various databases using a variety of information technologies. The problem with this knowledge is that it is easily lost if the information is stored in a manner that makes it difficult to retrieve, and so this knowledge can become implicit. The storing of information on the hard disk of personal computers by individual employees is a classic example of information that may well be totally lost when the employees leave the organization (Gunnlaugsdottir, 2003) . Another level of information (meta-information or information about information) is required to structure and categorize the information repositories for an enhanced utilization of the information objects.. Technically, there are exciting possibilities for the use of hypertext, groupware, intelligent agents, neural networks, advanced search techniques and other computational technologies to provide ''relevance retrieval'' access in large databases-retrieval which respects the meaning relationships among the stored items (Conklin, 2001) . Data manipulation and knowledge mapping representational tools are often designed to create metainformation according to the organizationÕs needs. Such tools include basic office tools, such as spreadsheets and outliners, annotation tools (Ginsburg and Kambil, 1999) , information retrieval systems such as collaborative filtering and browsing (Romano et al., 1999a; Hilmer and Dennis, 2000) , or ''knowledge mapping '' software. 1 Sharing and exchanging knowledge will not be a fruitful process unless it has been structured (Gunnlaugsdottir, 2003) .. The globalisation process and the spread of enterprise managers all over the world has led not only to new organization structures (Drucker, 1988) , but also to virtual project management and associated knowledge management problems (for further details, see Katzy et al., 2000) , and to the increasing need for distributed collaboration and decision-making support systems, driving a move towards business teams distributed along the dimensions of space, time, and technology. Large groups using group support systems (GSS) appear to benefit more than smaller groups (Vreede et al., 2003) , and research findings suggest that group support systems can facilitate knowledge acquisition (Kwok et al., 2000) , improve decision quality and quantity, enhance participant satisfaction (Dennis et al., 1996) , and reduce the cost and length of meetings. All of this helps to boost efficiency and productivity (some clear examples of this can be found in Vreede et al., 2003) .. GSS research is moving towards providing anytime/any-place/any-technology support for teams of any size (Romano et al., 1999b) . Distributed collaboration systems demand specific tools to support both space and time distributed groups. Different tools are needed from the ones used in regular synchronous meetings, whether they are held face-to-face (F2F), or not. During an asynchronous meeting, group members do not interact at the same time, even if they are at the same location. This line of work is extremely important to organizations with dispersed managers and workers, and vital to virtual project management. However, some problems with lower group satisfaction, decreased socialization among the group, and meeting coordination/facilitation problems have been pointed out as process drawbacks (Vreede et al., 2003; Lowry et al., 2002; Massey et al., 2002; Dufner et al., 2002) .. Research has presented many examples of systems that deal with this kind of working environment (for a detailed review, see Bafoutsou and Mentzas, 2002 ). Yet such systems normally use an active connection to a broader collaboration network (some kind of private network, or the internet, for example). Despite the fact that little research has been done in this area, it is the authorsÕ opinion that embedding new features that can enhance asynchronous usage allows work to proceed even without an active connection to the physical network, to the advantage of worker mobility. Therefore, groupware/collaboration tools may well bridge the gap by combining the two strategies. They create a platform where people can share codified, systematic knowledge and at the same time co-operate creatively in an organized environment. These systems even offer the opportunity to participate in discussions, to present and challenge ideas, and to vote anonymously, thus creating an environment where the participants can be frank and open without having to worry about any consequences (Abell and Oxbrow, 2001, p. 52) . This concern was embedded in the building of an argumentation model (as presented in Section 2) and the collaboration tool to implement it (the Collaboration Studio).. The Collaboration Studio (CS) argumentation model deals with four collaboration elements or document types, whose information and relationship network constitute a collaboration discussion. That set of documents consists of: a theme-the discussion generic description; attributes-used to define certain aspects that may require a more specific characterization within the theme; attribute characterizations-implemented on simple sentences, or on a more complex structure like a data matrix; and messages-whose objective is to comment, question or give some sort of explanation, and not to establish attribute characterizations. Each theme can contain multiple attributes, which in turn, can contain other attributes. Every attribute can also accept multiple characterizations.. The CS software system (described in Section 3) shares characteristics with other collaboration tools, such as synchronous and asynchronous support and both group working spaces and a local working space. However, its main purpose differs substantially from those tools due to the fact that instead of trying to achieve a sole document as the outcome of the joint work of several users, CS has a broader objective, which is to register (and to demonstrate) the ''path'' used to obtain a certain knowledge, in such a way that, when a collaboration document is finally drawn up, users are able to retrace all the steps leading to its achievement.. CS as an integral system comprises two elements: (i) a central memory that ensures global information persistence and server tasks; (ii) different workstations that ensure local information persistence. Central memory and workstations interact through a distributed architecture. The system allows discussions among a group of participants that include a coordinator. The discussion group is created by means of submissions, which are sent to the current coordinator for approval. The working mechanisms implemented within CS are perfectly transparent to the user, with implementation details remaining hidden, giving an appealing and user-friendly environment. This means that users do not have to worry about patterns of data distribution, or details of distribution management.. The system field-tests (Guerra, 2004) led to some important insights that were the major motivation for directing research towards divergence analysis in collaborative systems. The idea is to find a robust, but flexible, way to remove the ''locking'' approach, when multiple users asynchronously (with or without connection to a central memory) access and manipulate information objects, using divergence management, without losing consistency and structuring capabilities.. Though there are a number of different approaches to deal with different information versions or document replicates, none of them aims directly at knowledge management or organizational memory creation (Section 4). Our purpose is therefore to extend the previous research on divergence by embedding data structuring capabilities and an information linking structure with a high degree of expressiveness, in order to fill in the gap between divergence support features and divergence capabilities to help/improve knowledge creation and retrieval. We argue that this line of work can especially benefit decision agents in retrieving and recognizing the reasons that led users to diverge in their work, helping to better understand past decisions. We therefore aim at bringing closer GDSS to knowledge management theory, using divergence management.

O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B I


Lose and gain: impacts of ERK5 and JNK cascades on each other Abstract Kinase cascades in ERK5 (Extracellular signalregulated kinases) and JNK (c-Jun N-terminal kinases) signaling pathways mediate the sensing and processing of stimuli. Cross-talks between signaling cascades is a likely phenomenon that can cause apparently different biological responses from a single pathway, on its activation. Feedback loops have the potential to greatly alter the properties of a pathway and its response to stimuli. Based on enzyme kinetic reactions, mathematical models have been developed to predict and analyze the impacts of cross-talks and feedback loops in ERK5 and JNK cascades. It has been observed that, there is no significant impact on neither ERK5 activation nor JNKs' activation due to cross-talks between them. But it is due to cross-talks and feedback loops in ERK5 and JNK cascade, ERK5 gets activated in a transient manner in the absence of input signals. Planning to obtain the parameter values from the experimentalist and the result should be validated by experimental verification. Cells must be able to process multiple information in parallel and, they must also be able to combine this information in order to trigger the appropriate response (Berg et al. 2002) . This is achieved by wiring signaling pathways such that they can interact with each other, a phenomenon often called cross-talk (Sreenath et al. 2007 ). There have been attempts to quantify cross-talk in signaling networks. In one study cross-talk was categorized by a classification of the input-output relations of signaling networks. Quantification consisted of counting the occurrence of each category in a pair wise comparison of pathways. Another study quantified the degree of cross-talk between two pathways, by relating the number of realized interactions between two pathways to the number of hypothetically possible interactions (Schaber et al. 2006 ). This definition was restricted to pathways that do not share components. Both studies considered topological and structural properties of signaling networks and did not account for temporal and dynamic aspects. In our previous work, we have predicted the impacts of cross-talks between ERK5 and JNK kinase cascades (Sundaramurthy et al. 2009 ).. Complicated biochemical signaling pathways regulate the function of living cells. Such regulatory networks often have downstream components that provide input to components that act earlier in a pathway, creating feedback loops (Langlois et al. 1995) These feedback loops have the potential to greatly alter the properties of a pathway and how it responds to stimuli (Aronson et al. 1994; Bliss et al. 1982; Chen and Tan 2000; Lauffenburger 2001; Wolkenhauer and Mesarovic 2005) . Positive and negative feedback loops around MAPK (Mitogenactivated protein kinase) cascades have been a topic of interest in biological literature. For example, in (Ferrell and Machleder 1998) , one finds the study of positive feedback (on Mos by ERK) in the context of progesterone-induced oocyte maturation in frogs. In another example, Kholodenko (2000) and Wolkenhauer and Mesarovic (2005) looked at negative feedback, also in an ERK cascade, but affecting upstream proteins. JNK activation is an autocatalytic, in his work Bagowski and Ferrell (2001) has proposed two possible feedbacks, one from MAPK to MAPKK (Mitogen-activated protein kinase kinase) and the other from MAPKK to MAP-KKK(Mitogen-activated protein kinase kinase kinase) in JNK cascade in xenopus oocytes. We have identified the only possible feedback loops in JNK cascade are from JNK's to MKK's (unpublished work). In this work, based on enzyme kinetic reactions, mathematical models have been developed to predict and analyze the impacts of cross-talks and feedback loops in the ERK5 and JNK kinase cascades (Tables 1, 2 and 3) .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O B O B I O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B O B I O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O B I O B I O O O O O O O O O O O O B I I O O O O O O O O O O O O B O B I O O O O O O O O O O O O O


Semiautomatic evaluation of retrieval systems using document similarities Taking advantage of the well-known cluster hypothesis that "closely associated documents tend to be relevant to the same request", we can use inter-document similarity to provide more accurate and robust evaluation of retrieval systems. Using our method, we are able to accurately rank retrieval systems with up to 99% fewer relevance judgments than collected for the TREC conferences, and significantly more accurately than other algorithms given the same number of judgments. Test collection construction is an important part of information retrieval experimentation. But it is expensivealthough documents and queries are relatively easy to come by, relevance judgments are much harder. Assessors must be hired to read and judge documents, a process that is very resource-intensive. As a result, there has been a great deal of work on test collection construction and evaluation in the presence of incomplete or imperfect relevance judgments. Some of the work on the latter includes Buckley & Voorhees' bpref evaluation measure [2] , Yilmaz & Aslam's inferred average precision [12] , and Carterette et al.'s expected average precision [4] . For the former, there are two sub-fields: intelligent selection of documents for human assessors to judge (e.g. Cormack et al.'s Move-to-Front pooling [6] , Carterette et al.'s algorithm [4] , and Aslam et al.'s unbiased sampling method [1] ) and automatic evaluation without human assessors (e.g. Joachims' method based on clicks [9] and Jensen's method based on assigning relevance from web taxonomies [8] ). In this work we unite manual and automatic assessments of relevance with estimation methods for incomplete and imperfect judgments.. To do so we take advantage of van Rijsbergen's cluster hypothesis, which says that "closely associated documents tend to be relevant to the same request" [11] . In other words, a document that is similar to other relevant documents is likely to be relevant as well. The cluster hypothesis says nothing about documents that are nonrelevant or dissimilar, but these provide evidence as well: although there are many more ways a document can be nonrelevant than relevant, it still may be the case that a document similar to other nonrelevant documents is itself likely to be nonrelevant. Using this hypothesis to create probabilities that unjudged documents are relevant, we can estimate the differences between retrieval systems.. Our main contribution in this work is a model for evaluation based on document similarities. The most closely related previous work is that of Jensen [8] , who used manual web taxonomies to assign relevance to documents for web evaluation. Our work is complementary to his, showing how uncertainty due to these types of automatic relevance judgments can be incorporated formally. We have shown how document similarities can be used to evaluate retrieval systems with greatly reduced effort. The resulting similarity-based test collections provide more accurate evaluation results than the same number of pooled judgments and better confidence estimates than a simpler method of probability estimation.

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