bluuebunny/crossref_metadata_2025 huggingface dataset:
Nitrogen use efficiency as a tool to evaluate the development of ornamental cacti species
Authors: Silva Karina Gonçalves da , Ferreira Mauricio Lamano , Jocys Teresa , Kanashiro Shoey , Tavares Armando Reis
Published: December 2017

Nitrogen efficiency, along with associated indexes, is a widely used tool for assessing nutritional status in agricultural species. However, this parameter is not used in studies with ornamental plants, especially epiphytic cacti species. In particular, we know very little about the potential response of ornamental cacti to N absorption and use. Therefore, this study aimed to evaluate N use efficiency (NUE), along with its associated parameters, in three species of ornamental cacti under nitrogen nutrition. To accomplish this, Rhipsalis baccifera, Rhipsalis paradoxa and Hatiora salicornioides were fertilized by Hogland and Arnon nutrition solution modified and enriched with urea in the concentrations of 0, 33.3 or 66.6 mM N during 180 days. At the end of the experiment, efficiency indexes were calculated. Efficiency parameters varied according to species. R. baccifera presented the greatest dissimilarity among the species, with highest uptake efficiency (NUpE), but lowest use efficiency (NUtE) and biomass conversion (BCE). R. paradoxa presented high values for NUE, NUtE, BCE and physiological efficiency (NPE) at concentrations of 33.3 mM N, suggesting greater investment in biological processes with lower supply of N. H. salicornioides had the highest averages in most parameters measured. Our results show that these indexes provided important comparative baseline information on nutritional status and investment strategy, thus serving as a suitable analytical tool to increase knowledge about this group of ornamental plants.
Observations on John Garstang’s Excavations at Kazanlı Höyük (Cilicia) in 1937
Authors: Ahrens Alexander , Manuelli Federico
Published: December 2017

In 1937, British archaeologist John Garstang (1876–1956) excavated several trenches at the site of Kazanlı Höyük as part of the Neilson Expedition in Cilicia. The site is located in Plain Cilicia, approximately 2 km from the modern coastline (ca. 11 km east of modern-day Mersin, and 17 km west of Tarsus, roughly located by the ancient/modern road connecting these two cities). After these short trial excavations, however, Garstang’s interest shifted to the site of Mersin/Yumuktepe, where he then excavated for a number of years. Apart from two very brief preliminary reports on his excavations at Kazanlı Höyük, which were published in the journal “Annals of Archaeology and Anthropology of the University of Liverpool,” not much is known about his work at the site. Unpublished photographs kept in the Special Collections of the University College London (UCL) shed new light on Garstang’s work at the site.
In Defense of Nebuchadnezzar II the Warrior
Authors: Fantalkin Alexander
Published: December 2017

Recent scholarly trends diminish Nabû-kudurrī-uṣur’s II (Nebuchadnezzar’s II) military achievements in general while, at the same time, amplifying hypothetical martial accomplishments of Psammetichus II in the Levant. This paper argues that Nabû-kudurrī-uṣur’s military reputation should be maintained and that during his reign Egyptian advances in the Levant were decidedly modest.
A New Sale Document of the Ur III Period in the Sulaymaniyah Museum
Authors: Kamil Ari
Published: December 2017

This contribution presents a copy, transliteration and translation of a Neo-Sumerian cuneiform tablet from the Ur III administration. The document comes from Umma and is kept in the Archaeological Museum of as-Sulaimānīyah (Iraqi Kurdistan).
Siedlungskammer Kilikien
Authors: Rutishauser Susanne
Published: December 2017

An essential aspect of understanding past human activity is knowledge about the natural environment. Like almost no other landscape in Asia Minor, the Cilician Plain is defined by strong topographic contrasts: a large fertile plain surrounded by the sea and steep mountain ranges. The Cilician Plain is separated by a natural border into a western (Çukurova) and an eastern (Yukarıova) settlement cluster. All central places in the Çukurova have been located over the millennia at the foothills of the mountains. By contrast, the central places in the Yukarıova were less constant. The avulsion of the rivers can be seen as a reason for this development. However, there also exists for the construction of main Late Bronze and Iron Age monuments in the Cilician Plain a correlation with rivers and channels.
The Account of Nabû-šuma-iškun Revisited
Authors: Zadok Ran
Published: December 2017

Nabû-šuma-iškun is described as the emblematic malevolent king in the polemical pamphlet, authored in all probability by an anonymous member of the Urukean priestly establishment. However, certain measures taken by him are within the norms of ancient Near Eastern politics. In addition, accusations made against him by the author of the polemical pamphlet may be relativized. Nabû-šuma-iškun’s actions in the international sphere prove that he was a far-sighted statesman. It is demonstrated that important religious and political objectives of this king were not fundamentally different from those of later Babylonian rulers, including kings of the Neo-Babylonian Empire.
Lesevorschläge zu griechischen Steuerquittungen römischer Zeit aus Elephantine
Authors: Reiter Fabian
Published: December 2017

This article offers a series of textual remarks on Roman tax receipts from Elephantine on ostraca. It discusses readings and onomastics as well as chronological and tax-historical problems.
Association of adrenal hormone metabolites and mortality over a 6-year follow-up in COPD patients with acute exacerbation
Authors: Zurfluh Seline , Nickler Manuela , Ottiger Manuel , Steuer Christian , Kutz Alexander , Christ-Crain Mirjam , Zimmerli Werner , Thomann Robert , Hoess Claus , Henzen Christoph , Bernasconi Luca , Huber Andreas , Mueller Beat , Schuetz Philipp
Published: December 2017

Background:

The release of hormones from the adrenal gland is vital in acute and chronic illnesses such as chronic obstructive pulmonary disease (COPD) involving recurrent exacerbations. Using a metabolomic approach, we aim to investigate associations of different adrenal hormone metabolites with short- and long-term mortality in COPD patients.

Methods:

We prospectively followed 172 COPD patients (median age 75 years, 62% male) from a previous Swiss multicenter trial. At baseline, we measured levels of a comprehensive spectrum of adrenal hormone metabolites, including glucocorticoid, mineralocorticoid and androgen hormones by liquid chromatography coupled with tandem mass spectrometry (MS). We calculated Cox regression models adjusted for gender, age, comorbidities and previous corticosteroid therapy.

Results:

Mortality was 6.4% after 30 days and increased to 61.6% after 6 years. Higher initial androgen hormones predicted lower long-term mortality with significant results for dehydroepiandrosterone (DHEA) [adjusted hazard ratio (HR), 0.82; 95% confidence interval (CI), 0.70–0.98; p=0.026] and dehydroepiandrosterone sulfate (DHEA-S) (adjusted HR, 0.68; 95% CI, 0.50–0.91; p=0.009). An activation of stress hormones (particularly cortisol and cortisone) showed a time-dependent effect with higher levels pointing towards higher mortality at short term, but lower mortality at long term. Activation of the mineralocorticoid axis tended to be associated with increased short-term mortality (adjusted HR of aldosterone, 2.76; 95% CI, 0.79–9.65; p=0.111).

Conclusions:

Independent of age, gender, corticosteroid exposure and exacerbation type, adrenal hormones are associated with mortality at short and long term in patients with COPD exacerbation with different time-dependent effects of glucocorticoids, androgens and mineralocorticoids. A better physiopathological understanding of the causality of these effects may have therapeutic implications.
Miguel de Cervantes’s Don Quixote and John Barth’s The Sot-Weed Factor: A Deconstructive Reading
Authors: Martín-Párraga Javier
Published: December 2017

Miguel de Cervantes’s Don Quixote is one of the earliest and most influential novels in the history of Western literature. John Barth’s The Sot-Weed Factor, published almost three centuries later, can be considered as one of the most seminal postmodern novels ever written in the English language. The goal of this paper is to examine Cervantes’s influence on John Barth in particular and in American postmodernism from a more general point of view. For the Spanish genius’ footsteps on American postmodernism, a deconstructive reading will be employed. Consequently, concepts such as deconstruction of binary opposites, the role of the subaltern or how the distinction between history and story are paramount to both Cervantes and Barth will be used.
Czesław Miłosz’s Migrant Perspective in Rodzinna Europa [Native Realm]
Authors: Kołodziejczyk Ewa
Published: December 2017

The article traces the impact of Czesław Miłosz’s first American stay on his image of Central Europe in Rodzinna Europa [Native Realm]. In the United States, the post-war immigrant from Vilnius learned to perceive, understand and evaluate American culture; he also gained a new perspective on his region of Europe and Slavic immigrants. This experience enabled him to adopt an American point of view in his autobiographical essay. Following William Faulkner, Miłosz carries on an analysis of Eastern and Central Europe’s history and identities. The uses Western historical and sociological glossary to describe processes that formed his “native realm.” Analogically, the poet from pre-war Vilnius reflects on American multi-ethnicity and religious diversity from a Central European perspective. In Rodzinna Europa, Miłosz takes the position of a migrant translator and a two-way mediator between East and West.
Game Logic in the TV Series The Walking Dead: On Transmedial Plot Structures and Character Layouts
Authors: Lickhardt Maren
Published: December 2017

This paper discusses plot structures and characters of the The Walking Dead TV series which can be traced back to the influence of computer games and gameplay. It shall prove that the series’ narration as a transmedial phenomenon is characterised by ludic logic, although interactive aspects are omitted due to the intermedial change from games to a TV series. The characters’ twin layout as both civilised and archaic, the permanent necessity to make live-or-death decisions, as well as the struggle for survival, simulate aspects of adventure games in particular. The long duration of the series and its repetitive structure rely on PC gamers’ habits.
A joint regression modeling framework for analyzing bivariate binary data in R
Authors: Marra Giampiero , Radice Rosalba
Published: December 2017

We discuss some of the features of the R add-on package GJRM which implements a flexible joint modeling framework for fitting a number of multivariate response regression models under various sampling schemes. In particular,we focus on the case inwhich the user wishes to fit bivariate binary regression models in the presence of several forms of selection bias. The framework allows for Gaussian and non-Gaussian dependencies through the use of copulae, and for the association and mean parameters to depend on flexible functions of covariates. We describe some of the methodological details underpinning the bivariate binary models implemented in the package and illustrate them by fitting interpretable models of different complexity on three data-sets.
ImageServer, a Tool for On-line Processing and Analysis of Biological Images
Authors: Pisarev Andrei , Poustelnikova Ekaterina , Kozlov Konstantin , Myasnikova Ekaterina , Samsonova Maria , Reinitz John
Published: December 2017

We present a novel tool for processing and analysis of images of gene expression patterns stored in a relational database. This tool, known as ImageServer, is portable across different software/hardware platforms and supports both basic and subject domain oriented operations on images. The tool is used to process and analyze images stored in FlyEx database (http://urchin.spbcas.ru/flyex); software and documentation are available for download from http://urchin.spbcas.ru/downloads/IS/IS.htm.
Stochastic activation of the response regulator PhoB by noncognate histidine kinases
Authors: Zhou Lu , Grégori Gérald , Blackman Jennifer Masella , Robinson J. Paul , Wanner Barry L.
Published: December 2017

Two-component systems (TCS) are the most prevalent gene regulatory mechanism in bacteria. A typical TCS is comprised of a histidine kinase (HK) and a partner response regulator (RR). Specific environment signals lead to autophosphorylation of different HKs, which in turn act as phosphoryl donors for autophosphorylation of their partner RRs. Nonpartner HKs and RRs also interact, giving rise to cross regulation among TCSs in response to diverse signals.

PhoR (HK) and PhoB (RR) constitute the TCS for detection of environmental (extracellular) inorganic phosphate (Pi). The PhoR/PhoB TCS controls the expression of a large number of genes for acquisition of alternative phosphorus sources, including phoA, which encodes the non-specific phosphohydrolase bacterial alkaline phosphatase (Bap). Cross activation of PhoB by the nonpartner HK CreC is now a classic example of cross regulation among TCSs. A systematic search for other cross talking HKs revealed five additional HKs that activate (phosphorylate) PhoB (J. M. B. and B. L. W., unpublished data).

Examination of cross activation of PhoB by these non-partner HKs by flow cytometry at the single-cell level revealed a bimodal, “all-or-none,” distribution pattern for expression of a phoAp-gfp (green fluorescent protein) reporter fusion. Although the basis of the observed stochastic behavior is unclear, it seems to reflect an inherent property of TCSs. We propose that cells exploit the stochastic character of TCSs to achieve nongenetic (epigenetic) diversity within genetically homogeneous cell populations in order to facilitate adaptation to environmental changes.
A system for integrative and post-planned analysis of 2-DE/MS centered proteomics data
Authors: Wang Juhui , Caron Christophe , He Xuefeng , Carpentier Audrey , Mistou Michel-Yves , Trubuil Alain , Gitton Christophe , Henry Céline , Guillot Alain
Published: December 2017

Proteomic analysis is intrinsically an iterative, incremental process. Information is usually acquired gradually by researchers, and in different projects. At the same time, there are relatively few examples of biological data management systems which take into account this reality, most of them usually treat the experiment generated data as static and unchangeable: data are never reconsidered, or seldom, whereas technology becomes more powerful or that other researchers have brought information on data correction. And yet, post-planned analysis [21] which involves multiple iterations and subsequent re-investigations of previously prepared data might bring tremendous benefits.

Named PARIS (Proteomic Analysis and Resources Indexation System), the system we developed here seeks to address this requirement. Compliant with the majority of 2-DE analysis and MALDI-TOF based protein identification softwares, it automatically takes data from them and stores the raw and processed data in a relational database suitable for advanced exploration. Taking into account the standards proposed by PSI (Proteomics Standard Initiative), the system exports the stored data in XML format for data exchange and knowledge sharing. PARIS also manages information about experiments and their biological contexts, and allows the user to search and analyze a large data collection in a global manner. It provides tools for data integration and advanced, cross multi-experiment, multi-experimenter data exploration, and supports visual verification and correction of the analysis results. Implemented in Java, the system is platform independent, accessible to multiple users through Internet. It is also scalable for use for one or many laboratories, and therefore suitable to inter-institute collaborative work.

PARIS can be tested and downloaded at http://genome.jouy.inra.fr/paris
Managing Evidence from Multiple Gene Finding Resources via an XML-based Integration Architecture
Authors: Malousi Andigoni , Koutkias Vassilis , Maglaveras Nicos
Published: December 2017

While biological processes underlying gene expression are still under experimental research, computational gene prediction techniques have reached high level of sophistication with the employment of efficient intrinsic and extrinsic methods that identify protein-coding regions within query genomic sequences. Their ability though to delineate the exact exon boundaries is characterized by a trade off between sensitivity and specificity and still is prone to alternations in gene regulation during transcription and splicing and to inherent complexities introduced by the implemented methodology. Evaluation studies have shown that combinatorial approaches exhibit improved accuracy levels through the integration of evidence data from multiple resources that are further assessed in order to end up with the most probable gene assembly.

In this work, we present an integration and information handling architecture that exploits evidence derived from multiple gene finding resources, in order to generate machine-readable representations of optimal/suboptimal gene structure predictions, signal features identification and high scoring similarity matches. Unlike most combinatorial techniques, which end up with the most probable gene assembly, the objective of this architecture is to support advanced information handling mechanisms that may give more in depth insights on the underlying gene expression machinery and the alternations that may occur. Technically, XML was adopted to build and interchange structured data among the architecture’s components together with relevant technologies offering graphical representations and queries formulation/execution over single/multiple information sources.
Kinetic Modelling with the Systems Biology Modelling Environment SyBME
Authors: Junker Björn H. , Koschützki Dirk , Schreiber Falk
Published: December 2017

Kinetic modelling and simulation is an important approach in systems biology. While the focus of current modelling tools is on simulation, model development is a highly iterative process which is currently only partly supported. To support the development of biochemical models, their simulation, and graphical understanding, we designed and implemented SyBME, the Systems Biology Modelling Environment. Here we present the architecture and the main components of SyBME and show its use by modelling sucrose breakdown in developing potato tubers.
Functional classification of transcription factor binding sites: Information content as a metric
Authors: Reddy D. Ashok , Prasad B. V. L. S. , Mitra Chanchal K.
Published: December 2017

The information content (relative entropy) of transcription factor binding sites (TFBS) is used to classify the transcription factors (TFs). The TF classes are clustered based on the TFBS clustering using information content. Any TF belonging to the TF class cluster has a chance of binding to any TFBS of the clustered group. Thus, out of the 41 TFBS (in humans), perhaps only 5 -10 TFs may be actually needed and in case of mouse instead of 13 TFs, we may have actually 5 or so TFs. The JASPAR database of TFBS are used in this study. The experimental data on TFs of specific gene expression from TRRD database is also coinciding with our computational results. This gives us a new way to look at the protein classification- not based on their structure or function but by the nature of their TFBS.
CoryneRegNet 2: An Integrative Bioinformatics Approach for Reconstruction and Comparison of Transcriptional Regulatory Networks in Prokaryotes
Authors: Baumbach Jan , Brinkrolf Karina , Wittkop Tobias , Tauch Andreas , Rahmann Sven
Published: December 2017

CoryneRegNet is an ontology-based data warehouse of corynebacterial transcription factors and regulatory networks. Initially, it was designed to provide methods for the analysis and visualization of the gene regulatory network of Corynebacterium glutamicum. Now we integrated the genomes and transcriptional interactions of three other corynebacteria, C. diphtheriae, C. efficiens, and C. jeikeium into CoryneRegNet; providing comparative analysis and visualization with GraphVis. We also integrated the high-performance PSSM search tool PoSSuM search to detect potential transcription factor binding sites within and across species. As an application, we reconstruct in silico the regulatory network of the iron metabolism regulator DtxR in the four corynebacteria.

CoryneRegNet is freely accessible at https://www.cebitec.uni-bielefeld.de/groups/gi/software/coryneregnet/. The final slash (/) is mandatory. In order to use the GraphVis feature, Java (at least version 1.4.2) is required.
InSilico Proteomics System: Integration and Application of Protein and Protein-Protein Interaction Data using Microsoft .NET
Authors: Straßer Wolfgang , Siegl Doris , Önder Kamil , Bauer Johann
Published: December 2017

In the last decades, biological databases became the major knowledge resource for researchers in the field of molecular biology. The distribution of information among these databases is one of the major problems. An overview about the subject area of data access and representation of protein and protein-protein interaction data within public biological databases is described. For a comprehensive and consistent way of searching and analysing integrated protein and protein-protein interaction data, the InSilico Proteomics (ISP) project has been initiated. Its three main objectives are (1) to provide an integrated knowledge pool for data investigation and global network analysis functions for a better understanding of a cell’s interactome, (2) employment of public data for plausibility analysis and validation of in-house experimental data and (3) testing the applicability of Microsoft’s .NET architecture for bioinformatics applications. Data integrated into the ISP database can be queried through the Web portal PRIMOS (PRotein Interaction and MOlecule Search) which is freely available at http://biomis.fh-hagenberg.at/isp/primos.
Metabolite profiles as a reflection of physiological status – a methodological validation
Authors: Steinfath Matthias , Repsilber Dirk , Hische Manuela , Schauer Nicolas , Fernie Alisdair R. , Selbig Joachim
Published: December 2017

Biological ”omics” data comprise numerous variables (metabolites, gene expression, physiological quantities) and comparatively few samples. These samples represent either measurements for slightly different genotypes in identical environments, or for different environmental conditions affecting the same genotype. Given this kind of data, it is intriguing to ask for possible measurable associations between molecular variables and the phenotypical or physiological status.

To evaluate such correlations we need a model for the functional dependency of the physiological state on given molecular variables. Supervised machine learning methods such as neural networks, decision trees, or support vector machines may be used to reveal such correlations. The simplest model is certainly a linear approach. To investigate the association between molecular and phenotypical variables, we ask if the correlation between predictor and response is statistically significant, and how much of the phenotypical variance of the response can be explained by a given set of predictors. When confronted with a set of molecular data not all of them are generally relevant for each physiological trait. Given this fact the problem of feature selection arises.

Different regression methods have been developed to answer this question: Ordinary Least Squares (OLS) yields an unbiased solution, but normally has a high mean square error. In particular, there is no dimension reduction included in this method and, hence, overfitting is a critical problem. In contrast, Principle Component Regression (PCR) offers such a dimension reduction, however, the principle components are found without considering the response. Partial Least Squares Regression (PLSR) is utilised as an alternative method since it considers the variance within the predictors as well as between predictors and response, whilst Ridge Regression is a further alternative worthy of consideration.

In our study we applied these methods to data resulting from a tomato metabolite experimental series. Comparison of the results for this dataset with experimentally relevant correlation structure between variables and samples allows us to test the relative merits of the regression methods with respect to the questions raised above. Given certain prerequisite knowledge it also allows us to conjecture the true biological correlation. Our results show that under most circumstances OLS is worst with respect to prediction. However, the ranking of methods seems to change considerably if the question of feature selection is considered. Understanding and discussing these differences is a relevant contribution to the task of choice of suitable approach of correlation analysis for “omics” datasets with respect to the biological interpretation in question.
Modeling Microarray Data: Interpreting and communicating the biological results
Authors: Pittelkow Y. E. , Wilson S. R.
Published: December 2017

Various statistical models have been proposed for detecting differential gene expression in data from microarray experiments. Given such detection, we are usually interested in describing the differential expression patterns. Due to the large number of genes that are typically analysed in microarray experiments, possibly more than ten thousand, the tasks of interpretation and communication of all the corresponding statistical models pose a considerable challenge, except perhaps in the simplest experiment involving only two groups. A further challenge is to find methods to summarize the resulting models. These challenges increase with experimental complexity.

Biologists often wish to sort genes into ‘classes’ with similar response profiles/patterns. So, in this paper we describe a likelihood approach for assigning genes to these different class patterns for data from a replicated experimental design.

The number of potential patterns increases very quickly as the number of combinations in the experimental design increases. In a two group experimental design there are only three patterns required to describe the mean response: up, down and no difference. For a factorial design with three treatments there are 13 different patterns, and with four levels there are 75 potential patterns to be considered, and so on.

The approach is applied to the identification of differential response patterns in gene expression from a microarray experiment using RNAextracted from the leaves of Arabidopsis thaliana plants.

We compare patterns of response found using additive and multiplicative models. A multiplicative model is more commonly used in the statistical analysis of microarray data because of the variance stabilizing properties of the logarithmic function. Then the error structure of the model is taken to be log-Normal. On the other hand, for the additive model the gene expression value is modeled directly as being from a gamma distribution which successfully accounts for the constant coefficient of variation often observed.

Appropriate visualization displays for microarray data are important as a way of communicating the patterns of response amongst the genes. Here we use graphical ‘icons’ to represent the patterns of up/down and no response and two alternative displays, the Gene-plot and a grid layout to provide rapid overall summaries of the gene expression patterns.
Inference of Gene Coexpression Networks by Integrative Analysis across Microarray Experiments
Authors: Elo Laura L. , Aittokallio Tero
Published: December 2017

We improve the reliability of detecting coexpressed gene pairs from microarray data by introducing a novel probe-level quality-weighted similarity measure for combining data across different Affymetrix experiments. In construction of gene coexpression networks, the proposed procedure is less sensitive to noise than the corresponding single-experiment approaches or the conventional integrative approaches, even when a relatively small number of samples and conditions is available. The present results indicate how the accumulated microarray data can be effectively exploited to increase the quality of the inferred networks. In particular, we demonstrate its biological relevance in identifying coexpressions in mouse T helper cell differentiation.
Combining biomedical knowledge and transcriptomic data to extract new knowledge on genes
Authors: Guérin Emilie , Marquet Gwenaëlle , Chabalier Julie , Troadec Marie-Bérengère , Guguen-Guillouzo Christiane , Loréal Olivier , Burgun Anita , Moussouni Fouzia
Published: December 2017

In biomedical research, interpretation of microarray data requires confrontation of data and knowledge from heterogeneous resources, either in the biomedical domain or in genomics, as well as restitution and analysis methods adapted to huge amounts of data. We present a combined approach that relies on two components: BioMeKE annotates sets of genes using biomedical GO and UMLS concepts, and GEDAW, a Gene Expression Data Warehouse, uses BioMeKE to enrich experimental results with biomedical concepts, thus performing complex analyses of expression measurements through analysis workflows. The strength of our approach has been demonstrated within the framework of analysis of data resulting from the liver transcriptome study. It allowed new genes potentially associated with liver diseases to be highlighted.
Exact and Approximate Stochastic Simulations of the MAPK Pathway and Comparisons of Simulations’ Results
Authors: Purutçuoǧlu Vilda , Wit Ernst
Published: December 2017

The MAPK (mitogen-activated protein kinase) or its synonymous ERK (extracellular signal regulated kinase) pathway whose components are Ras, Raf, and MEK proteins with many biochemical links, is one of the major signalling systems involved in cellular growth control of eukaryotes including cell proliferation, transformation, differentiation, and apoptosis. In this study we describe the MAPK/ERK pathway via (quasi) biochemical reactions and then implement the pathway by a stochastic Markov process. A novelty of our approach is to use multiple parametrizations in order to deal with molecules for which localization in the cell is an intricate part of the dynamic process and to describe the protein using different binding sites and various phosphorylations. We simulate the system by exact and different approximate simulations, e.g. via the Poisson τ-leap, the Binomial τ-leap and the diffusion methods, in which we introduce a new updating plan for dependent columns of the diffusion matrix. Finally we compare the results of different algorithms by the current biological knowledge and find out new relations about this complex system.
An assessment of machine and statistical learning approaches to inferring networks of protein-protein interactions
Authors: Browne Fiona , Wang Haiying , Zheng Huiru , Azuaje Francisco
Published: December 2017

Protein-protein interactions (PPI) play a key role in many biological systems. Over the past few years, an explosion in availability of functional biological data obtained from high-throughput technologies to infer PPI has been observed. However, results obtained from such experiments show high rates of false positives and false negatives predictions as well as systematic predictive bias. Recent research has revealed that several machine and statistical learning methods applied to integrate relatively weak, diverse sources of large-scale functional data may provide improved predictive accuracy and coverage of PPI. In this paper we describe the effects of applying different computational, integrative methods to predict PPI in Saccharomyces cerevisiae. We investigated the predictive ability of combining different sets of relatively strong and weak predictive datasets. We analysed several genomic datasets ranging from mRNA co-expression to marginal essentiality. Moreover, we expanded an existing multi-source dataset from S. cerevisiae by constructing a new set of putative interactions extracted from Gene Ontology (GO)- driven annotations in the Saccharomyces Genome Database. Different classification techniques: Simple Naive Bayesian (SNB), Multilayer Perceptron (MLP) and K-Nearest Neighbors (KNN) were evaluated. Relatively simple classification methods (i.e. less computing intensive and mathematically complex), such as SNB, have been proven to be proficient at predicting PPI. SNB produced the “highest” predictive quality obtaining an area under Receiver Operating Characteristic (ROC) curve (AUC) value of 0.99. The lowest AUC value of 0.90 was obtained by the KNN classifier. This assessment also demonstrates the strong predictive power of GO-driven models, which offered predictive performance above 0.90 using the different machine learning and statistical techniques. As the predictive power of single-source datasets became weaker MLP and SNB performed better than KNN. Moreover, predictive performance saturation may be reached independently of the classification models applied, which may be explained by predictive bias and incompleteness of existing “Gold Standards”. More comprehensive and accurate PPI maps will be produced for S. cerevisiae and beyond with the emergence of largescale datasets of better predictive quality and the integration of intelligent classification methods.
Prediction of transcription factor binding to DNA using rule induction methods
Authors: Huss Mikael , Nordström Karin
Published: December 2017

In this study, we seek to develop a predictive model for finding the strength of binding between a particular transcription factor (TF) variant and a particular DNA target variant. The DNA binding paired domains of the Pax transcription factors, which are our main focus, show seemingly fuzzy and degenerate binding to various DNA targets, and paired domain-DNA binding is not a problem well suited for previously proposed algorithms. Here, we introduce a simple way to use rule induction for predicting the strength of TFDNA binding. We have created a dataset consisting of 597 example cases for paired domain-DNA binding by collecting information about all published and quantified interactions between TF and DNA sequence variants. Application of the rule induction based method on this dataset yields a high, although far from ideal accuracy of 69.7% (based on cross-validation), but perhaps more importantly, several useful rules for predicting the binding strength have been found. Although the primary motivation for introducing the rule induction based methods is the lack of efficient algorithms for paired domain-DNA binding prediction, we also show that the method can be applied with some success to a more well-studied TF-DNA binding prediction task involving the early growth response (EGR) TF family.
The GeWare data warehouse platform for the analysis of molecular-biological and clinical data
Authors: Rahm Erhard , Kirsten Toralf , Lange Jörg
Published: December 2017

We introduce the GeWare data warehouse platform for the integrated analysis of clinical information, microarray data and annotations within large biomedical research studies. Clinical data is obtained from a commercial study management system while publicly available data is integrated using a mediator approach. The platform utilizes a generic approach to manage different types of annotations. We outline the overall architecture of the platform, its implementation as well as the main processing and analysis workflows.
From MIN model to ordinary differential equations
Authors: Yartseva A. , Devillers R. , Klaudel H. , Képès F.
Published: December 2017

Biological interaction networks can be modeled using the Modular Interaction Network (MIN) formalism, which provides an intermediary modeling level between the biological and mathematical ones. MIN focuses on a simple but structured and versatile representation of biological knowledge, without targeting a particular analysis or simulation technique. In this paper, we propose a translation procedure which, starting from a MIN specification of a biological system, generates its representation in ordinary differential equations (ODEs) allowing to study the dynamics of the system. The translation is illustrated on a classical benchmark: the λ phage genetic switch.
The OXL format for the exchange of integrated datasets
Authors: Taubert Jan , Sieren Klaus Peter , Hindle Matthew , Hoekman Berend , Winnenburg Rainer , Philippi Stephan , Rawlings Chris , Köhler Jacob
Published: December 2017

A prerequisite for systems biology is the integration and analysis of heterogeneous experimental data stored in hundreds of life-science databases and millions of scientific publications. Several standardised formats for the exchange of specific kinds of biological information exist. Such exchange languages facilitate the integration process; however they are not designed to transport integrated datasets. A format for exchanging integrated datasets needs to i) cover data from a broad range of application domains, ii) be flexible and extensible to combine many different complex data structures, iii) include metadata and semantic definitions, iv) include inferred information, v) identify the original data source for integrated entities and vi) transport large integrated datasets. Unfortunately, none of the exchange formats from the biological domain (e.g. BioPAX, MAGE-ML, PSI-MI, SBML) or the generic approaches (RDF, OWL) fulfil these requirements in a systematic way.

We present OXL, a format for the exchange of integrated data sets, and detail how the aforementioned requirements are met within the OXL format. OXL is the native format within the data integration and text mining system ONDEX. Although OXL was developed with the ONDEX system in mind, it also has the potential to be used in several other biological and non-biological applications described in this paper.

Availability: The OXL format is an integral part of the ONDEX system which is freely available under the GPL at http://ondex.sourceforge.net/. Sample files can be found at http://prdownloads.sourceforge.net/ondex/ and the XML Schema at http://ondex.svn.sf.net/viewvc/*checkout*/ondex/trunk/backend/data/xml/ondex.xsd.
VINEdb: a data warehouse for integration and interactive exploration of life science data
Authors: Hariharaputran Sridhar , Töpel Thoralf , Brockschmidt Björn , Hofestädt Ralf
Published: December 2017

Control of cell proliferation, differentiation, activation and cell removal is crucial for the development and existence of multi-cellular organisms. Apoptosis, or programmed cell death, is a major control mechanism by which cells die and is also important in controlling cell number and proliferation as part of normal development. Molecular networks that regulate these processes are critical targets for drug development, gene therapy, and metabolic engineering. The molecular interactions involved in this and other processes are analyzed and annotated by experts and stored as data in different databases. The key task is to integrate, manage and visualize these data available from different sources and present them in a user-comprehensible manner.

Here we present VINEdb, a data warehouse developed to interact with and to explore integrated life science data. Extendable open source data warehouse architecture enables platform-independent usability of the web application and the underlying infrastructure. A high degree of transparency and up-to-dateness is ensured by a monitor component to control and update the data from the sources. Furthermore, the system is supported by a visualization component to allow interactive graphical exploration of the integrated data. We will use apoptotic pathway and caspase-3 as a case study to show capability and usability of our approach. VINEdb is available at http://tunicata.techfak.unibielefeld.de/VINEdb/.
Developmental Anatomy Ontology of Zebrafish: an Integrative semantic framework
Authors: Belmamoune Mounia , Verbeek Fons J.
Published: December 2017

Integration of information is quintessential to make use of the wealth of bioinformatics resources. One aspect of integration is to make databases interoperable through well annotated information. With new databases one strives to store complementary information and such results in collections of heterogeneous information systems. Concepts in these databases need to be connected and ontologies typically provide a common terminology to share information among different resources.

Our focus of research is the zebrafish and we have developed several information systems in which ontologies are crucial. Pivot is an ontology describing the developmental anatomy, referred to as the Developmental Anatomy Ontolgoy of Zebrafish (DAOZ). The anatomical and temporal concepts are provided by the Zebrafish Information Network (ZFIN) and proven within the research community. We have constructed a 3D digital atlas of zebrafish development based on histology; the atlas is series of volumetric models; in each instance, every volume element is assigned to an anatomical term. Complementing the atlas we developed an information system with 3D patterns of gene expression in zebrafish development based on marker genes. The spatial and temporal annotations to these 3D images are drawn from the ontology that we have designed. In its design the DAOZ ontology is structured as a Directed Acyclic Graph (DAG). Such is required to find unique concept paths and prevent self referencing.

As we need to address the ontology in a direct manner, the DAG structure is transferred to a database. The database is used in the integration of our databases that share concepts at different levels of aggregation. In order to make sure that sufficient levels of aggregation for applications in mind are present, the original vocabulary was enriched with more relations and concepts. Both databases can now be addressed with the same unique terms and co-occurrence and co-expression of genes can be readily extracted from the databases. Integration can be further extended to the ZFIN resource and also by including ontologies that relate to gene/gene expression (e.g. Gene Ontology). In this manner, interoperable information retrieval from heterogeneous databases can be realized. This greatly facilitates processing complex information and retrieving relations in the data through machine learning approaches.
Monophyletic clustering and characterization of protein families
Authors: Zhang Jian , Zhao Zhiyuan , Evershed Jennifer , Li Guoying
Published: December 2017

A protein family contains sequences that are evolutionarily related. Generally, this is reflected by sequence similarity. There have been many attempts to organize the set of protein families into evolutionarily homogenous clusters using certain clustering methods. How do we characterize these clusters? How can we cluster protein families using these characterizations? In this work, these questions were addressed by use of a concept called group-wide co-evolution, and was exemplified by some real and simulated protein family data. The results have shown that the trend of a group of monophyletic proteins might be characterized by a normal distribution, while the strength and variability of this trend can be described by the sample mean and variance of the observed correlation coefficients after a suitable transformation. To exploit this property, we have developed a monophyletic clustering method called monophyletic k−medoids clustering. A software package written in R has been made available at http://www.kent.ac.uk/ims/personal/jz .
A Methodology for Comparative Functional Genomics
Authors: Alam Intikhab , Cornell Mike , Soanes Darren M. , Hedeler Cornelia , Wong Han Min , Rattray Magnus , Hubbard Simon J. , Talbot Nicholas J. , Oliver Stephen G. , Paton Norman W.
Published: December 2017

The continuing and rapid increase in the number of fully sequenced genomes is creating new opportunities for comparative studies. However, although many genomic databases store data from multiple organisms, for the most part they provide limited support for comparative genomics. We argue that refocusing genomic data management to provide more direct support for comparative studies enables systematic identification of important relationships between species, thereby increasing the value that can be obtained from sequenced genomes. The principal result of the paper is a methodology, in which comparative analyses are constructed over a foundation based on sequence clusters and evolutionary relationships. This methodology has been applied in a systematic study of the fungi, and we describe how comparative analyses have been implemented as an analysis library over the e-Fungi data warehouse.
Functional and Transcriptional Coherency of Modules in the Human Protein Interaction Network
Authors: Futschik Matthias E. , Chaurasia Gautam , Tschaut Anna , Russ Jenny , Babu M. Madan , Herzel Hanspeter
Published: December 2017

Modularity is a major design principle in interaction networks. Various studies have shown that protein interaction networks in prokaryotes and eukaryotes display a modular structure. A majority of the studies have been performed for the yeast interaction network, for which data have become abundant. The systematic examination of the human protein interaction network, however, is still in an early phase. To assess whether the human interaction network similarly displays a modular structure, we assembled a large protein network consisting of over 30,000 interactions. More than 670 modules were subsequently identified based on the detection of cliques. Inspection showed that these modules included numerous known protein complexes. The extracted modules were scrutinized for their coherency with respect to function, localization and expression, thereby allowing us to distinguish between stable and dynamic modules. Finally, the examination of the overlap between modules identified key proteins linking distinct molecular processes.