nils-herrmann/clean_scirepeval huggingface dataset:
Bayesian Network Approach to Cell Signaling Pathway Modeling

The modeling of cellular signaling pathways is an emerging field. Sachs et al. illustrate the application of Bayesian networks to an example cellular pathway involving the activation of focal adhesion kinase (FAK) and extracellular signal-regulated kinase (ERK) in response to fibronectin binding to an integrin. They describe how to use the analysis to select from among proposed models, formulate hypotheses regarding component interactions, and uncover potential dynamic changes in the interactions between these components. Although the data sets currently available for this example problem are too small to definitively point to a particular model, the approach and results provide a glimpse into the power that these methods will achieve once the technology for obtaining the necessary data becomes readily available.
A new approach to analyzing gene expression time series data

We present algorithms for time-series gene expression analysis that permit the principled estimation of unobserved time-points, clustering, and dataset alignment. Each expression profile is modeled as a cubic spline (piecewise polynomial) that is estimated from the observed data and every time point influences the overall smooth expression curve. We constrain the spline coefficients of genes in the same class to have similar expression patterns, while also allowing for gene specific parameters. We show that unobserved time-points can be reconstructed using our method with 10-15% less error when compared to previous best methods. Our clustering algorithm operates directly on the continuous representations of gene expression profiles, and we demonstrate that this is particularly effective when applied to non-uniformly sampled data. Our continuous alignment algorithm also avoids difficulties encountered by discrete approaches. In particular, our method allows for control of the number of degrees of freedom of the warp through the specification of parameterized functions, which helps to avoid overfitting. We demonstrate that our algorithm produces stable low-error alignments on real expression data and further show a specific application to yeast knockout data that produces biologically meaningful results.
Unsupervised Active Learning in Large Domains

Active learning is a powerful approach to analyzing data effectively. We show that the feasibility of active learning depends crucially on the choice of measure with respect to which the query is being optimized. The standard information gain, for example, does not permit an accurate evaluation with a small committee, a representative subset of the model space. We propose a surrogate measure requiring only a small committee and discuss the properties of this new measure. We devise, in addition, a bootstrap approach for committee selection. The advantages of this approach are illustrated in the context of recovering (regulatory) network models.
Continuation Methods for Mixing Heterogenous Sources

A number of modern learning tasks involve estimation from heterogeneous information sources. This includes classification with labeled and unlabeled data as well as other problems with analogous structure such as competitive (game theoretic) problems. The associated estimation problems can be typically reduced to solving a set of fixed point equations (consistency conditions). We introduce a general method for combining a preferred information source with another in this setting by evolving continuous paths of fixed points at intermediate allocations. We explicitly identify critical points along the unique paths to either increase the stability of estimation or to ensure a significant departure from the initial source. The homotopy continuation approach is guaranteed to terminate at the second source, and involves no combinatorial effort. We illustrate the power of these ideas both in classification tasks with labeled and unlabeled data, as well as in the context of a competitive (min-max) formulation of DNA sequence motif discovery.
Exact MAP Estimates by (Hyper)tree Agreement

We describe a method for computing provably exact maximum a posteriori (MAP) estimates for a subclass of problems on graphs with cycles. The basic idea is to represent the original problem on the graph with cycles as a convex combination of tree-structured problems. A convexity argument then guarantees that the optimal value of the original problem (i.e., the log probability of the MAP assignment) is upper bounded by the combined optimal values of the tree problems. We prove that this upper bound is met with equality if and only if the tree problems share an optimal configuration in common. An important implication is that any such shared configuration must also be the MAP configuration for the original problem. Next we develop a tree-reweighted max-product algorithm for attempting to find convex combinations of tree-structured problems that share a common optimum. We give necessary and sufficient conditions for a fixed point to yield the exact MAP estimate. An attractive feature of our analysis is that it generalizes naturally to convex combinations of hypertree-structured distributions.
Tree-based reparameterization analysis of belief propagation and related algorithms for approximate inference on graphs with cycles

Although it is straightforward to compute the marginals of a distribution p(x) defined by a tree-structured graphical model, this same task is often difficult for graphs with cycles. The belief propagation (BP) or sum-product algorithm is an approximate method for computing such marginals; it is used in various applications (e.g., iterative decoding of turbo and LDPC codes). Belief propagation is typically presented and analyzed as a sequence of message-passing operations. We develop a different conceptual perspective that involves reparameterizing the original distribution in terms of so-called pseudomarginals on cliques of the graph. This view gives rise to a simple characterization of the fixed points, as well as an exact expression and bounds on the error for an arbitrary graph with cycles.
Information Regularization with Partially Labeled Data

Classification with partially labeled data requires using a large number of unlabeled examples (or an estimated marginal P(x)), to further constrain the conditional P(y|x) beyond a few available labeled examples. We formulate a regularization approach to linking the marginal and the conditional in a general way. The regularization penalty measures the information that is implied about the labels over covering regions. No parametric assumptions are required and the approach remains tractable even for continuous marginal densities P(x). We develop algorithms for solving the regularization problem for finite covers, establish a limiting differential equation, and exemplify the behavior of the new regularization approach in simple cases.
Automatic Feature Induction for Text Classification

All classifiers require a set of features that can be used to distinguish between different examples. In some cases, such as determining whether a chess position is a winning position, the features are clear (the positions of the chess pieces). In other cases, such as text, they are less clear. A document is simply a string of characters. Standard practice dictates that documents should be broken into words, common, uninformative words should be removed and a stemmer is to be applied for removal of common endings (such as “-s” and “-ing”). However, this processing is insufficient for many languages and many modern-day classification problems. For example, documents about the XWindow system include an unusually large number of words that begin with the letter “x,” but traditional processing obscures this fact. Unsolicitied commercial e-mail (“spam”) can often be identified by the use of foreign languages, a disproportionate number of exclamation points or capital letters. Again, such information will often be discarded.
Maximum Likelihood Markov Hypertrees

One popular class of such models are Markov networks, which use an undirected graph to represent dependencies among variables. Markov networks of low tree-width (i.e. having a triangulation with small cliques ) allow efficient computations, and are useful as learned probability models . A well studied case is that in which the dependency structure is known in advance. In this case the underlying graph is built based on prior knowledge, and a maximum likelihood Markov network over this specific graph is sought .
Using Graphical Models and Genomic Expression Data to Statistically Validate Models of Genetic Regulatory Networks

We propose a model-driven approach for analyzing genomic expression data that permits genetic regulatory networks to be represented in a biologically interpretable computational form. Our models permit latent variables capturing unobserved factors, describe arbitrarily complex (more than pair-wise) relationships at varying levels of refinement, and can be scored rigorously against observational data. The models that we use are based on Bayesian networks and their extensions. As a demonstration of this approach, we utilize 52 genomes worth of Affymetrix GeneChip expression data to correctly differentiate between alternative hypotheses of the galactose regulatory network in S. cerevisiae. When we extend the graph semantics to permit annotated edges, we are able to score models describing relationships at a finer degree of specification.
Improving Multi-class Text Classification with Naive Bayes Improving Multi-class Text Classification with Naive Bayes Contents

There are numerous text documents available in electronic form. More and more are becoming available every day. Such documents represent a massive amount of information that is easily accessible. Seeking value in this huge collection requires organization; much of the work of organizing documents can be automated through text classification. The accuracy and our understanding of such systems greatly influences their usefulness. In this paper, we seek 1) to advance the understanding of commonly used text classification techniques, and 2) through that understanding, improve the tools that are available for text classification. We begin by clarifying the assumptions made in the derivation of Naive Bayes, noting basic properties and proposing ways for its extension and improvement. Next, we investigate the quality of Naive Bayes parameter estimates and their impact on classification. Our analysis leads to a theorem which gives an explanation for the improvements that can be found in multiclass classification with Naive Bayes using Error-Correcting Output Codes. We use experimental evidence on two commonly-used data sets to exhibit an application of the theorem. Finally, we show fundamental flaws in a commonly-used feature selection algorithm and develop a statistics-based framework for text feature selection. Greater understanding of Naive Bayes and the properties of text allows us to make better use of it in text classification.  Tommi Jaakkola Title: Assistant Professor of Electrical Engineering and Computer Science
Stable Mixing of Complete and Incomplete Information

An increasing number of parameter estimation tasks involve the use of at least two information sources, one complete but limited, the other abundant but incomplete. Standard algorithms such as EM (or em) used in this context are unfortunately not stable in the sense that they can lead to a dramatic loss of accuracy with the inclusion of incomplete observations. We provide a more controlled solution to this problem through differential equations that govern the evolution of locally optimal solutions (fixed points) as a function of the source weighting. This approach permits us to explicitly identify any critical (bifurcation) points leading to choices unsupported by the available complete data. The approach readily applies to any graphical model in O(n) time where n is the number of parameters. We use the naive Bayes model to illustrate these ideas and demonstrate the effectiveness of our approach in the context of text classification problems. Acknowledgements This work was supported in part by Nippon Telegraph and Telephone Corporation, by ARO MURI grant DAAD19-00-1-0466, and by NSF ITR grant IIS-0085836.
Maximum-likelihood estimation of optimal scaling factors for expression array normalization

Data from expression arrays must be comparable before it can be analyzed rigorously on a large scale. Accurate normalization improves the comparability of expression data because it seeks to account for sources of variation obscuring the underlying variation of interest. Undesirable variation in reported expression levels originates in the preparation and hybridization of the sample as well as in the manufacture of the array itself, and may differ depending on the array technology being employed. Published research to date has not characterized the degree of variation associated with these sources, and results are often reported without tight statistical bounds on their significance. We analyze the distributions of reported levels of exogenous control species spiked into samples applied to 1280 Affymetrix arrays. We develop a model for explaining reported expression levels under an assumption of primarily multiplicative variation. To compute the scaling factors needed for normalization, we derive maximum likelihood and maximum a posteriori estimates for the parameters characterizing the multiplicative variation in reported spiked control expression levels. We conclude that the optimal scaling factors in this context are weighted geometric means and determine the appropriate weights. The optical scaling factor estimates so computed can be used for subsequent array normalization.
Partially labeled classification with Markov random walks

To classify a large number of unlabeled examples we combine a limited number of labeled examples with a Markov random walk representation over the unlabeled examples. The random walk representation exploits any low dimensional structure in the data in a robust, probabilistic manner. We develop and compare several estimation criteria/algorithms suited to this representation. This includes in particular multi-way classification with an average margin criterion which permits a closed form solution. The time scale of the random walk regularizes the representation and can be set through a margin-based criterion favoring unambiguous classification. We also extend this basic regularization by adapting time scales for individual examples. We demonstrate the approach on synthetic examples and on text classification problems.
Active Information Retrieval

In classical large information retrieval systems, the system responds to a user initiated query with a list of results ranked by relevance, The users may further refine their query as needed. This process may result in a lengthy correspondence without conclusion. We propose an alternative active learning approach, where the system responds to the initial user's query by successively probing the user for distinctions at multiple levels of abstraction. The system's initiated queries are optimized for speedy recovery and the user is permitted to respond with multiple selections or may reject the query. The information is in each case unambiguously incorporated by the system and the subsequent queries are adjusted to minimize the need for further exchange. The system's initiated queries are subject to resource constraints pertaining to the amount of information that can be presented to the user per iteration.
Clustering and efficient use of unlabeled examples

Efficient learning with partially labeled data involves extracting structure from large unlabeled set and combining this information with limited labeled examples. A typical albeit unstated assumption in this context associates separable clusters in the unlabeled set with unique but unknown labels. When this assumption is valid, labeled examples are needed only to the extent that they can facilitate labeling of the clusters. We capture and formalize this intuition in a conditional probability model where soft clusters serve to regularize the labeling of the unlabeled examples. Clustering is achieved by defining a Markov diffusion process (cf. Tishby and Slonim, NIPS 2000). The associated time scale of this process determines the effective size of the clusters and is chosen through a margin based criterion that guarantees unambiguous classification of examples. We relate the time scale to the mixing time of the Markov process and extend the basic idea by combining multiple time scales to maximize classification accuracy. We demonstrate the performance of the approach on both real and synthetic datasets. 1 Representation based on Markov diffusion To achieve good learning performance, the data must be encoded in a suitable representation matched to the learning algorithm. Typically, we are provided data points in a space, and a distance metric that measures pairwise similarity between points. The provided distance metric is often quite accurate locally, as it is relatively easy to characterize small perturbations in the data. However, over larger distances, the given metric is frequently inadequate, and hurts the performance of the many learning algorithms that rely on global distances. Fortunately, in problems with many data points (with or without labels), we can use the locally accurate metric to construct an improved global distance measure that reflects the density of the data. For example, the data may lie on a submanifold of the space, revealed by the density, and we should measure distances along the manifold. Intuitively, the distances are smaller in directions of high density, and larger in low-density directions. We define a Markov diffusion process based on the locally accurate metric. The local metric defines probabilities of transitioning between two nearby points in one timestep, and we construct the global distance as the probability of transitioning between two points in t timesteps. Thus, we consider all the paths of length t on this graph. Formally, consider a set of points fx1; : : : ;xNg that need not be labeled. Construct a graph whose nodes correspond to data points, and whose undirected edges correspond to one step transitions. We only allow such immediate transitions from a point to its neighbors. Specifically, for each point, connect it with an undirected edge to its K nearest neighbors. Points in high density centers can be neighbors of many points and end up with more than K edges. Self-transitions back to the point itself are also included. Let the probability of transitioning from a state i at time t to a neighbor k at time t+ 1 be
Fast optimal leaf ordering for hierarchical clustering

We present the first practical algorithm for the optimal linear leaf ordering of trees that are generated by hierarchical clustering. Hierarchical clustering has been extensively used to analyze gene expression data, and we show how optimal leaf ordering can reveal biological structure that is not observed with an existing heuristic ordering method. For a tree with n leaves, there are 2(n-1) linear orderings consistent with the structure of the tree. Our optimal leaf ordering algorithm runs in time O(n(4)), and we present further improvements that make the running time of our algorithm practical.
Tree-based reparameterization for approximate inference on loopy graphs

We develop a tree-based reparameterization framework that provides a new conceptual view of a large class of iterative algorithms for computing approximate marginals in graphs with cycles. It includes belief propagation (BP), which can be reformulated as a very local form of reparameterization. More generally, we consider algorithms that perform exact computations over spanning trees of the full graph. On the practical side, we find that such tree reparameterization (TRP) algorithms have convergence properties superior to BP. The reparameterization perspective also provides a number of theoretical insights into approximate inference, including a new characterization of fixed points; and an invariance intrinsic to TRP/BP. These two properties enable us to analyze and bound the error between the TRP/BP approximations and the actual marginals. While our results arise naturally from the TRP perspective, most of them apply in an algorithm-independent manner to any local minimum of the Bethe free energy. Our results also have natural extensions to more structured approximations [e.g., 1, 2].
Sparse Matrix Factorization of Gene Expression Data

A fuel cell apparatus comprising a cell block and a oxygen-containing gas purifier placed by the side of cell block, a fuel recirculation circuit, an electrolyte recirculation circuit, a static device for oxygen-containing gas draft, fed through a fuel leakage circuit and a heat exchanger exchanging heat between the hot electrolyte issuing from the cell block and the cold oxygen-containing gas purifying liquid issuing from the oxygen-containing gas purifier, thereby causing the respective motions of the electrolyte and the purifying liquid by double thermosiphon effect.
A Comprehensive Approach to Fusion for Microsensor Networks: Distributed and Hierarchical Inference, Communication, and Adaption REPORT DOCUMENTATION PAGE

This final report summarizes the research and activities under the ODDRE summarizes highlights of the impact our work has already had; lists many of the awards and recognition we have received; describes interactions with and transitions to DoD activities as well as the infrastructure we have put in place (including the program website which will continue into the future); and briefly summarizes the major research accomplishments we have had. The report closes with a complete listing of the very substantial list of publications that have resulted from research supported by this project.
Kernel Expansions with Unlabeled Examples

Modern classification applications necessitate supplementing the few available labeled examples with unlabeled examples to improve classification performance. We present a new tractable algorithm for exploiting unlabeled examples in discriminative classification. This is achieved essentially by expanding the input vectors into longer feature vectors via both labeled and unlabeled examples. The resulting classification method can be interpreted as a discriminative kernel density estimate and is readily trained via the EM algorithm, which in this case is both discriminative and achieves the optimal solution. We provide, in addition, a purely discriminative formulation of the estimation problem by appealing to the maximum entropy framework. We demonstrate that the proposed approach requires very few labeled examples for high classification accuracy.
A Discriminative Framework for Detecting Remote Protein Homologies

A new method for detecting remote protein homologies is introduced and shown to perform well in classifying protein domains by SCOP superfamily. The method is a variant of support vector machines using a new kernel function. The kernel function is derived from a generative statistical model for a protein family, in this case a hidden Markov model. This general approach of combining generative models like HMMs with discriminative methods such as support vector machines may have applications in other areas of biosequence analysis as well.