nils-herrmann/clean_scirepeval huggingface dataset:
Learning BayesianNetwork Structur e fr om Massive Datasets: The “SparseCandidate” Algorithm

LearningBayesiannetworksis often castas an optimizationproblem,wherethe computational task is to find a structurethat maximizesa statistically motivatedscore.By andlarge,existing learningtoolsaddressthis optimizationproblem usingstandardheuristicsearchtechniques. Since the searchspaceis extremelylarge, suchsearch procedurescanspendmostof thetimeexamining candidatesthatareextremelyunreasonable. This problembecomescriticalwhenwedealwith data sets that are large either in the numberof instances, or thenumberof attributes. In this paper , we introducean algorithm that achievesfasterlearningby restrictingthesearch space.This iterative algorithmrestrictsthe parentsof eachvariableto belongto a small subsetof candidates. We thensearchfor a network that satisfiestheseconstraints.The learnednetwork is thenusedfor selectingbettercandidates for thenext iteration.We evaluatethis algorithm bothon synthetic andreal-life data.Our results show thatit is significantlyfasterthanalternati ve searchprocedureswithout lossof quality in the learnedstructures.
Context-specific Bayesian clustering for gene expression data

The recent growth in genomic data and measurement of genome-wide expression patterns allows to examine gene regulation by transcription factors using computational tools. In this work, we present a class of mathematical models that help in understanding the connections between transcription factors and functional classes of genes based on genetic and genomic data. These models represent the joint distribution of transcription factor binding sites and of expression levels of a gene in a single model. Learning a combined probability model of binding sites and expression patterns enables us to improve the clustering of the genes based on the discovery of putative binding sites and to detect which binding sites and experiments best characterize a cluster. To learn such models from data, we introduce a new search method that rapidly learns a model according to a Bayesian score. We evaluate our method on synthetic data as well as on real data and analyze the biological insights it provides.
A structural EM algorithm for phylogenetic inference

A central task in the study of evolution is the reconstruction of a phylogenetic tree from sequences of current-day taxa. A well supported approach to tree reconstruction performs maximum likelihood (ML) analysis. Unfortunately, searching for the maximum likelihood phylogenetic tree is computationally expensive. In this paper, we describe a new algorithm that uses Structural-EM for learning maximum likelihood trees. This algorithm is similar to the standard EM method for estimating branch lengths, except that during iterations of this algorithms the topology is improved as well as the branch length. The algorithm performs iterations of two steps. In the E-Step, we use the current tree topology and branch lengths to compute expected sufficient statistics, which summarize the data. In the M-Step, we search for a topology that maximizes the likelihood with respect to these expected sufficient statistics. As we show, searching for better topologies inside the M-step can be done efficiently, as opposed to standard search over topologies. We prove that each iteration of this procedure increases the likelihood of the topology, and thus the procedure must converge. We evaluate our new algorithm on both synthetic and real sequence data, and show that it is both dramatically faster and finds more plausible trees than standard search for maximum likelihood phylogenies.
Class discovery in gene expression data

Recent studies (Alizadeh et al, ; Bittner et al,[5]; Golub et al, [11]) demonstrate the discovery of putative disease subtypes from gene expression data. The underlying computational problem is to partition the set of sample tissues into statistically meaningful classes. In this paper we present a novel approach to class discovery and develop automatic analysis methods. Our approach is based on statistically scoring candidate partitions according to the overabundance of genes that separate the different classes. Indeed, in biological datasets, an overabundance of genes separating known classes is typically observed. we measure overabundance against a stochastic null model. This allows for highlighting subtle, yet meaningful, partitions that are supported on a small subset of the genes.
Using simulated annealing we explore the space of all possible partitions of the set of samples, seeking partitions with statistically significant overabundance of differentially expressed genes. We demonstrate the performance of our methods on synthetic data, where we recover planted partitions. Finally, we turn to tumor expression datasets, and show that we find several highly pronounced partitions.
Plausibility measures and default reasoning

We introduce a new approach to modeling uncertainty based on plausibility measures. This approach is easily seen to generalize other approaches to modeling uncertainty, such as probability measures, belief functions, and possibility measures. We focus on one application of plausibility measures in this paper: default reasoning. In recent years, a number of different semantics for defaults have been proposed, such as preferential structures, ε-semantics, possibilistic structures, and &kgr;-rankings, that have been shown to be characterized by the same set of axioms, known as the KLM properties. While this was viewed as a surprise, we show here that it is almost inevitable. In the framework of plausibility measures, we can give a necessary condition for the KLM axioms to be sound, and an additional condition necessary and sufficient to ensure that the KLM axioms are complete. This additional condition is so weak that it is almost always met whenever the axioms are sound. In particular, it is easily seen to hold for all the proposals made in the literature.
Inferring subnetworks from perturbed expression profiles

Genome-wide expression profiles of genetic mutants provide a wide variety of measurements of cellular responses to perturbations. Typical analysis of such data identifies genes affected by perturbation and uses clustering to group genes of similar function. In this paper we discover a finer structure of interactions between genes, such as causality, mediation, activation, and inhibition by using a Bayesian network framework. We extend this framework to correctly handle perturbations, and to identify significant subnetworks of interacting genes. We apply this method to expression data of S. cerevisiae mutants and uncover a variety of structured metabolic, signaling and regulatory pathways.
Incorporating Expressive Graphical Models in VariationalApproximations: Chain-graphs and Hidden Variables

Global variational approximation methods in graphical models allow efficient approximate inference of complex posterior distributions by using a simpler model. The choice of the approximating model determines a tradeoff between the complexity of the approximation procedure and the quality of the approximation. In this paper, we consider variational approximations based on two classes of models that are richer than standard Bayesian networks, Markov networks or mixture models. As such, these classes allow to find better tradeoffs in the spectrum of approximations. The first class of models are chain graphs, which capture distributions that are partially directed. The second class of models are directed graphs (Bayesian networks) with additional latent variables. Both classes allow representation of multi-variable dependencies that cannot be easily represented within a Bayesian network.
A Simple Hyper-Geometric Approach for Discovering Putative Transcription Factor Binding Sites

A central issue in molecular biology is understanding the regulatory mechanisms that control gene expression. The recent flood of genomic and post-genomic data opens the way for computational methods elucidating the key components that play a role in these mechanisms. One important consequence is the ability to recognize groups of genes that are co-expressed using microarray expression data. We then wish to identify in-silico putative transcription factor binding sites in the promoter regions of these gene, that might explain the coregulation, and hint at possible regulators. In this paper we describe a simple and fast, yet powerful, two stages approach to this task. Using a rigorous hypergeometric statistical analysis and a straightforward computational procedure we find small conserved sequence kernels. These are then stochastically expanded into PSSMs using an EM-like procedure. We demonstrate the utility and speed of our methods by applying them to several data sets from recent literature. We also compare these results with those of MEME when run on the same sets.
Statistical benchmarking and class discovery in gene expression data

s: Session III 9 6 Wolfl, Stefan [63] Screening of lung cancer samples using gene expression profiling Stefan Wolfl1, Larissa Odyvanova1, Torsten Kroll1, Jorg Sanger2 & Joachim Clement1 1Klinik für Innere Medizin, Universität Jena, Jena, Germany 2Institut für Pathologie, Bad Berka, Germany Tumor initiation and progression is a complex process that differs not only between different tumors but also between distinct areas of a single tumor. Therefore it is important to analyze key players in cellular metabolism in tumors simultaneously. Lung cancers are one of the dominant causes of tumor-related death. The prospect for understanding the genetic background of these tumors and their complex pathological picture makes lung cancer a particularly interesting target for functional genomic analysis. We compared tissue samples from lung adenocarcinomas and squamous cell carcinomas with samples from adjacent tumor-free resection margins. Tissue samples were collected at surgery and immediately frozen in liquid nitrogen, and all sample material was histologically classified. For expression array analysis we purified total RNA using an optimized CsClcushion centrifugation protocol. We hybridized complementary DNA array membranes (Clontech Atlas Arrays and UniGene-based membranes from RZPD, Berlin) with 33P cDNA and collected data by phosphoimaging. Comparison of the expression patterns led us to the following observations: (1) Tumor and tumorfree resection margins show a high degree of correlation in their expression patterns. (2) Comparisons between tumors or between tumor-free tissue samples show no obvious correlation. (3) The similarity between tumors and normal tissue from one patient is more pronounced in adenocarcinoma than in squamous cell carcinoma.(4) Filtering genes that represent plate epithelial carcinoma versus adenocarcinoma led to two potential discriminating candidate genes, those coding for interleukin-1α and granzyme A.
Gaussian Process Networks

In this paper we address the problem of learning the structure of a Bayesian network in domains with continuous variables. This task requires a procedure for comparing different candidate structures. In the Bayesian framework, this is done by evaluating the marginal likelihood of the data given a candidate structure. This term can be computed in closed-form for standard parametric families (e.g., Gaussians), and can be approximated, at some computational cost, for some semi-parametric families (e.g., mixtures of Gaussians). 
 
We present a new family of continuous variable probabilistic networks that are based on Gaussian Process priors. These priors are semiparametric in nature and can learn almost arbitrary noisy functional relations. Using these priors, we can directly compute marginal likelihoods for structure learning. The resulting method can discover a wide range of functional dependencies in multivariate data. We develop the Bayesian score of Gaussian Process Networks and describe how to learn them from data. We present empirical results on artificial data as well as on real-life domains with non-linear dependencies.
Likelihood Computations Using Value Abstraction

In this paper, we use evidence-specific value abstraction for speeding Bayesian networks inference. This is done by grouping variable values and treating the combined values as a single entity. As we show, such abstractions can exploit regularities in conditional probability distributions and also the specific values of observed variables. To formally justify value abstraction, we define the notion of safe value abstraction and devise inference algorithms that use it to reduce the cost of inference. Our procedure is particularly useful for learning complex networks with many hidden variables. In such cases, repeated likelihood computations are required for EM or other parameter optimization techniques. Since these computations are repeated with respect to the same evidence set, our methods can provide significant speedup to the learning procedure. We demonstrate the algorithm on genetic linkage problems where the use of value abstraction sometimes differentiates between a feasible and non-feasible solution.
Scoring Genes for Relevance

Recent molecular level studies that compare different classes of disease conditions produce labeled gene expression data. We examine scoring methods that are useful in mining such gene expression data for genes that have biological relevance to the condition studied. Relevance information is useful in identifying genes driving the biological process, in selecting small subsets of genes with diagnostic potential, and in better understanding the condition studied and its relationship to known or hypothesized biochemical pathways. We present the scoring methods; describe a process for computing the corresponding p-values; and finally, present results from application to actual cancer gene expression data. These include applying classification techniques employing varying relevance based selected sets of genes.
Tissue classification with gene expression profiles

Constantly improving gene expression profiling technologies are expected to provide understanding and insight into cancer related cellular processes. Gene expression data is also expected to significantly and in the development of efficient cancer diagnosis and classification platforms. In this work we examine two sets of gene expression data measured across sets of tumor and normal clinical samples One set consists of 2,000 genes, measured in 62 epithelial colon samples . The second consists of ≈ 100,000 clones, measured in 32 ovarian samples (unpublished, extension of data set described in [26]).
We examine the use of scoring methods, measuring separation of tumors from normals using individual gene expression levels. These are then coupled with high dimensional classification methods to assess the classification power of complete expression profiles. We present results of performing leave-one-out cross validation (LOOCV) experiments on the two data sets. employing SVM [8], AdaBoost [13] and a novel clustering based classification technique. As tumor samples can differ from normal samples in their cell-type composition we also perform LOOCV experiments using appropriately modified sets of genes, attempting to eliminate the resulting bias.
We demonstrate success rate of at least 90% in tumor vs normal classification, using sets of selected genes, with as well as without cellular contamination related members. These results are insensitive to the exact selection mechanism, over a certain range.
Using Bayesian Networks to Analyze Expression Data

DNA hybridization arrays simultaneously measure the expression level for thousands of genes. These measurements provide a "snapshot" of transcription levels within the cell. A major challenge in computational biology is to uncover, from such measurements, gene/protein interactions and key biological features of cellular systems. In this paper, we propose a new framework for discovering interactions between genes based on multiple expression measurements. This framework builds on the use of Bayesian networks for representing statistical dependencies. A Bayesian network is a graph-based model of joint multivariate probability distributions that captures properties of conditional independence between variables. Such models are attractive for their ability to describe complex stochastic processes and because they provide a clear methodology for learning from (noisy) observations. We start by showing how Bayesian networks can describe interactions between genes. We then describe a method for recovering gene interactions from microarray data using tools for learning Bayesian networks. Finally, we demonstrate this method on the S. cerevisiae cell-cycle measurements of Spellman et al. (1998).
Using Bayesian networks to analyze expression data

DNA hybridization arrays simultaneously measure the expression level for thousands of genes. These measurements provide a “snapshot” of transcription levels within the cell. A major challenge in computational biology is to uncover, from such measurements, gene/protein interactions and key biological features of cellular systems.
In this paper, we propose a new framework for discovering interactions between genes based on multiple expression measurements This framework builds on the use of Bayesian networks for representing statistical dependencies. A Bayesian network is a graph-based model of joint multi-variate probability distributions that captures properties of conditional independence between variables. Such models are attractive for their ability to describe complex stochastic processes, and for providing clear methodologies for learning from (noisy) observations.
We start by showing how Bayesian networks can describe interactions between genes. We then present an efficient algorithm capable of learning such networks and statistical method to assess our confidence in their features. Finally, we apply this method to the S. cerevisiae cell-cycle measurements of Spellman et al.  to uncover biological features
Tissue classification with gene expression profiles.

Constantly improving gene expression profiling technologies are expected to provide understanding and insight into cancer-related cellular processes. Gene expression data is also expected to significantly aid in the development of efficient cancer diagnosis and classification platforms. In this work we examine three sets of gene expression data measured across sets of tumor(s) and normal clinical samples: The first set consists of 2,000 genes, measured in 62 epithelial colon samples (Alon et al., 1999). The second consists of approximately equal to 100,000 clones, measured in 32 ovarian samples (unpublished extension of data set described in Schummer et al. (1999)). The third set consists of approximately equal to 7,100 genes, measured in 72 bone marrow and peripheral blood samples (Golub et al, 1999). We examine the use of scoring methods, measuring separation of tissue type (e.g., tumors from normals) using individual gene expression levels. These are then coupled with high-dimensional classification methods to assess the classification power of complete expression profiles. We present results of performing leave-one-out cross validation (LOOCV) experiments on the three data sets, employing nearest neighbor classifier, SVM (Cortes and Vapnik, 1995), AdaBoost (Freund and Schapire, 1997) and a novel clustering-based classification technique. As tumor samples can differ from normal samples in their cell-type composition, we also perform LOOCV experiments using appropriately modified sets of genes, attempting to eliminate the resulting bias. We demonstrate success rate of at least 90% in tumor versus normal classification, using sets of selected genes, with, as well as without, cellular-contamination-related members. These results are insensitive to the exact selection mechanism, over a certain range.
Data Analysis with Bayesian Networks: A Bootstrap Approach

In recent years there has been significant progress in algorithms and methods for inducing Bayesian networks from data. However, in complex data analysis problems, we need to go beyond being satisfied with inducing networks with high scores. We need to provide confidence measures on features of these networks: Is the existence of an edge between two nodes warranted? Is the Markov blanket of a given node robust? Can we say something about the ordering of the variables? We should be able to address these questions, even when the amount of data is not enough to induce a high scoring network. In this paper we propose Efron's Bootstrap as a computationally efficient approach for answering these questions. In addition, we propose to use these confidence measures to induce better structures from the data, and to detect the presence of latent variables.
Model based Bayesian Exploration

Reinforcement learning systems are often concerned with balancing exploration of untested actions against exploitation of actions that are known to be good. The benefit of exploration can be estimated using the classical notion of Value of Information - the expected improvement in future decision quality arising from the information acquired by exploration. Estimating this quantity requires an assessment of the agent's uncertainty about its current value estimates for states. 
 
In this paper we investigate ways to represent and reason about this uncertainty in algorithms where the system attempts to learn a model of its environment. We explicitly represent uncertainty about the parameters of the model and build probability distributions over Q-values based on these. These distributions are used to compute a myopic approximation to the value of information for each action and hence to select the action that best balances exploration and exploitation
Plausibility measures and default reasoning: an overview

We introduce a new approach to modeling uncertainty based on plausibility measures. This approach is easily seen to generalize other approaches to modeling uncertainty, such as probability measures, belief functions, and possibility measures. We then consider one application of plausibility measures: default reasoning. In recent years, a number of different semantics for defaults have been proposed, such as preferential structures, /spl epsiv/-semantics, possibilistic structures, and /spl kappa/-rankings, that have been shown to be characterized by the same set of axioms, known as the KLM properties. While this was viewed as a surprise, we show here that it is almost inevitable. In the framework of plausibility measures, we can give a necessary condition for the KLM axioms to be sound, and an additional condition necessary and sufficient to ensure that the KLM axioms are complete. This additional condition is so weak that it is almost always met whenever the axioms are sound. In particular, it is easily seen to hold for all the proposals made in the literature. Finally, we show that plausibility measures provide an appropriate basis for examining first-order default logics.
Modeling Belief in Dynamic Systems, Part II: Revisions and Update

The study of belief change has been an active area in philosophy and AI. In recent years two special cases of belief change, belief revision and belief update, have been studied in detail. In a companion paper (Friedman & Halpern, 1997), we introduce a new framework to model belief change. This framework combines temporal and epistemic modalities with a notion of plausibility, allowing us to examine the change of beliefs over time. In this paper, we show how belief revision and belief update can be captured in our framework. This allows us to compare the assumptions made by each method, and to better understand the principles underlying them. In particular, it shows that Katsuno and Mendelzon's notion of belief update (Katsuno & Mendelzon, 1991a) depends on several strong assumptions that may limit its applicability in artificial intelligence. Finally, our analysis allow us to identify a notion of minimal change that underlies a broad range of belief change operations including revision and update.