nils-herrmann/clean_scirepeval huggingface dataset:
Comparative analysis of algorithms for signal quantitation from oligonucleotide microarrays

Recent years' exponential increase in DNA microarrays experiments has motivated the development of many signal quantitation (SQ) algorithms. These algorithms perform various transformations on the actual measurements aimed to enable researchers to compare readings of different genes quantitatively within one experiment and across separate experiments. However, it is relatively unclear whether there is a 'best' algorithm to quantitate microarray data. The ability to compare and assess such algorithms is crucial for any downstream analysis. In this work, we suggest a methodology for comparing different signal quantitation algorithms for gene expression data. Our aim is to enable researchers to compare the effect of different SQ algorithms on the specific dataset they are dealing with. We combine two kinds of tests to assess the effect of an SQ algorithm in terms of signal to noise ratio. To assess noise, we exploit redundancy within the experimental dataset to test the variability of a given SQ algorithm output. For the effect of the SQ on the signal we evaluate the overabundance of differentially expressed genes using various statistical significance tests.



We demonstrate our analysis approach with three SQ algorithms for oligonucleotide microarrays. We compare the results of using the dChip software and the RMAExpress software to the ones obtained by using the standard Affymetrix MAS5 on a dataset containing pairs of repeated hybridizations. Our analysis suggests that dChip is more robust and stable than the MAS5 tools for about 60% of the genes while RMAExpress is able to achieve an even greater improvement in terms of signal to noise, for more than 95% of the genes.
Blood transcriptional signatures of multiple sclerosis: Unique gene expression of disease activity

Multiple sclerosis (MS) is a central nervous system disease with an unpredictable course and outcome. Peripheral blood mononuclear cells (PBMCs) are involved in the disease pathogenesis and induce active demyelination. Using oligonucleotide microarrays, we identified a statistically significant transcriptional signature of 1,109 genes in PBMCs from 26 MS patients, irrespective of disease activation state or immunomodulatory treatment. This signature contains genes that implicate underlying processes involved in MS pathogenesis including T‐cell activation and expansion, inflammation, and apoptosis. Another transcriptional signature of 721 genes involved in cellular recruitment, epitope spreading, and escape from regulatory immune surveillance identified MS patients in acute relapse compared with remission. Our results offer new opportunity for understanding the mechanisms involved in MS and indicate that gene expression patterns in PBMCs contain information about a remote‐target disease process that may be useful for diagnosis and future tailoring of therapeutic strategies for MS.
"Ideal Parent" Structure Learning for Continuous Variable Networks

In recent years, there is a growing interest in learning Bayesian networks with continuous variables. Learning the structure of such networks is a computationally expensive procedure, which limits most applications to parameter learning. This problem is even more acute when learning networks with hidden variables. We present a general method for significantly speeding the structure search algorithm for continuous variable networks with common parametric distributions. Importantly, our method facilitates the addition of new hidden variables into the network structure efficiently. We demonstrate the method on several data sets, both for learning structure on fully observable data, and for introducing new hidden variables during structure search.
Discriminative Learning of Composite Transcriptional Regulatory Modules

A central goal of molecular biology is to uncover transcription regulation mechanisms that govern gene expression. Transcription factors play an important role in those mechanisms, as they affect the transcription rates of genes. Often, such regulatory circuits involve not only one transcription factor but rather several factors that act in concert to modulate the transcription of genes. The recent advances in high-throughput assays, such as microarray experiments and Chromatin Immunoprecipitation, allow us to infer groups of genes that are co-expressed or coregulated. The challenge is to use this wealth of information to gain insights about transcriptional regulation. In this dissertation, we present a procedure for locating regulatory complexes in promoter regions. A regulatory complex represents the binding sites of a pair of transcription factors that act in cooperation. Our procedure takes a discriminative approach, searching for regulatory complexes that are overabundant in the promoter regions of the target group of co-expressed genes and are infrequent in the control group of genes outside the target group. By doing this, we filter out phenomena that are shared among both groups, ideally leaving us with the core motifs. We demonstrate the applicability of our method for finding regulatory complexes in a genome-wide analysis of the yeast genome.
Functional Genomics of Lung Disease

Jeffrey A. Whitsett, Cindy J. Bachurski, Kathleen C. Barnes, Paul A. Bunn, Jr., Lisa M. Case, Donald N. Cook, Denise Crooks, Mark W. Duncan, Lori Dwyer-Nield, Robert C. Elston, Michael B. Fessler, Wilbur A. Franklin, Nir Friedman,  Lisa M. Henning, Gary L. Johnson, Kamon Kawkitinarong, Robert L. Keith, Thomas R. Korfhagen, George D. Leikauf, Stephen B. Liggett, Kenneth C. Malcolm, Alvin M. Malkinson, Thomas J. Mariani, Susan A. McDowell, Dennis W. McGraw, Mario Medvedovic, Joel Moss, Lawrence M. Nogee, Stephanie Nonas, Gustavo Pacheco-Rodriguez, Lyle J. Palmer, David G. Peters, Daniel R. Prows, Susan Redline, Aviv Regev, Maureen A. Sartor, David A. Schwartz, Edwin K. Silverman, Wendy K. Steagall, Robert S. Stearman, Angelo Taveira-DaSilva, Jay W. Tichelaar, Craig R. Tomlinson, Katsuya Tsukada, Timothy E. Weaver, Susan E. Wert, Scott C. Wesselkamper, G. S. Worthen, Yan Xu, Laura Zerbe, Yi Zhang, Yingze Zhang, Augustine M. K. Choi, and Naftali Kaminski
Inferring Cellular Networks Using Probabilistic Graphical Models

High-throughput genome-wide molecular assays, which probe cellular networks from different perspectives, have become central to molecular biology. Probabilistic graphical models are useful for extracting meaningful biological insights from the resulting data sets. These models provide a concise representation of complex cellular networks by composing simpler submodels. Procedures based on well-understood principles for inferring such models from data facilitate a model-based methodology for analysis and discovery. This methodology and its capabilities are illustrated by several recent applications to gene expression data.
Bayesian Network Classifiers

Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive with state-of-the-art classifiers such as C4.5. This fact raises the question of whether a classifier with less restrictive assumptions can perform even better. In this paper we evaluate approaches for inducing classifiers from data, based on the theory of learning Bayesian networks. These networks are factored representations of probability distributions that generalize the naive Bayesian classifier and explicitly represent statements about independence. Among these approaches we single out a method we call Tree Augmented Naive Bayes (TAN), which outperforms naive Bayes, yet at the same time maintains the computational simplicity (no search involved) and robustness that characterize naive Bayes. We experimentally tested these approaches, using problems from the University of California at Irvine repository, and compared them to C4.5, naive Bayes, and wrapper methods for feature selection.
Inferring quantitative models of regulatory networks from expression data

Genetic networks regulate key processes in living cells. Various methods have been suggested to reconstruct network architecture from gene expression data. However, most approaches are based on qualitative models that provide only rough approximations of the underlying events, and lack the quantitative aspects that are critical for understanding the proper function of biomolecular systems.



We present fine-grained dynamical models of gene transcription and develop methods for reconstructing them from gene expression data within the framework of a generative probabilistic model. Unlike previous works, we employ quantitative transcription rates, and simultaneously estimate both the kinetic parameters that govern these rates, and the activity levels of unobserved regulators that control them. We apply our approach to expression datasets from yeast and show that we can learn the unknown regulator activity profiles, as well as the binding affinity parameters. We also introduce a novel structure learning algorithm, and demonstrate its power to accurately reconstruct the regulatory network from those datasets.
Tissue Classi cation with Gene Expression Pro les

Constantly improving gene expression pro ling technologies are expected to provide understanding and insight into cancer-related cellular processes. Gene expression data is also expected to signi cantly aid in the development of ef cient cancer diagnosis and classi cation platforms. In this work we examine three sets of gene expression data measured across sets of tumor(s) and normal clinical samples: The  rst set consists of 2,000 genes, measured in 62 epithelial colon samples (Alon et al., 1999). The second consists of 100,000 clones, measured in 32 ovarian samples (unpublished extension of data set described in Schummer et al. (1999)). The third set consists of 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 classi cation methods to assess the classi cation power of complete expression pro les. We present results of performing leave-one-out cross validation (LOOCV) experiments on the three data sets, employing nearest neighbor classi er, SVM (Cortes and Vapnik, 1995), AdaBoost (Freund and Schapire, 1997) and a novel clusteringbased classi cation technique. As tumor samples can differ from normal samples in their cell-type composition, we also perform LOOCV experiments using appropriately modi ed sets of genes, attempting to eliminate the resulting bias. We demonstrate success rate of at least 90% in tumor versus normal classi cation, 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.
Modeling dependencies in protein-DNA binding sites

The availability of whole genome sequences and high-throughput genomic assays opens the door for in silico analysis of transcription regulation. This includes methods for discovering and characterizing the binding sites of DNA-binding proteins, such as transcription factors. A common representation of transcription factor binding sites is a position specific score matrix (PSSM). This representation makes the strong assumption that binding site positions are independent of each other. In this work, we explore Bayesian network representations of binding sites that provide different tradeoffs between complexity (number of parameters) and the richness of dependencies between positions. We develop the formal machinery for learning such models from data and for estimating the statistical significance of putative binding sites. We then evaluate the ramifications of these richer representations in characterizing binding site motifs and predicting their genomic locations. We show that these richer representations improve over the PSSM model in both tasks.
Human and porcine early kidney precursors as a new source for transplantation

Kidney transplantation has been one of the major medical advances of the past 30 years. However, tissue availability remains a major obstacle. This can potentially be overcome by the use of undifferentiated or partially developed kidney precursor cells derived from early embryos and fetal tissue. Here, transplantation in mice reveals the earliest gestational time point at which kidney precursor cells, of both human and pig origin, differentiate into functional nephrons and not into other, non-renal professional cell types. Moreover, successful organogenesis is achieved when using the early kidney precursors, but not later-gestation kidneys. The formed, miniature kidneys are functional as evidenced by the dilute urine they produce. In addition, decreased immunogenicity of the transplants of early human and pig kidney precursors compared with adult kidney transplants is demonstrated in vivo. Our data pinpoint a window of human and pig kidney organogenesis that may be optimal for transplantation in humans.
Probabilistic models for identifying regulation networks

Microarray-based hybridization methods techniques allow to simultaneously measure the expression level for thousands of genes. Such measurements contain information about many different aspects of gene regulation and function, and indeed this type of experiments has become a central tool in biological research. A major computational challenge is finding ways to extract new biological understanding from this wealth of data. Our goal is to uncover the causal structure of the interactions between genes, with the aim of understanding the regulatory processes that bring about the observed expression patterns. I will argue that one way of addressing this question is a Bayesian framework, where we treat the measured expression level of each gene as a random variable and each regulatory interaction as a probabilistic dependency between such variables. In my talk, I will describe an ongoing project to use Bayesian networks and extensions of them to model such dependencies. In the talk I will explain the basic foundations of the approach, the possible choices in defining the modeling language, the methods we use to learn models from data, and finally how we interpret the learned models. This latter stage includes validation against known biology, and constructing new hypotheses about the role of unknown genes.
The Information Bottleneck EM Algorithm

Learning with hidden variables is a central challenge in probabilistic graphical models that has important implications for many real-life problems. The classical approach is using the Expectation Maximization (EM) algorithm. This algorithm, however, can get trapped in local maxima. In this paper we explore a new approach that is based on the Information Bottleneck principle. In this approach, we view the learning problem as a tradeoff between two information theoretic objectives. The first is to make the hidden variables uninformative about the identity of specific instances. The second is to make the hidden variables informative about the observed attributes. By exploring different tradeoffs between these two objectives, we can gradually converge on a high-scoring solution. As we show, the resulting, Information Bottleneck Expectation Maximization (IB-EM) algorithm, manages to find solutions that are superior to standard EM methods.
A Structural EM Algorithm for Phylogenetic Inference

A central task in the study of molecular evolution is the reconstruction of a phylogenetic tree from sequences of current-day taxa. The most established approach to tree reconstruction is maximum likelihood (ML) analysis. Unfortunately, searching for the maximum likelihood phylogenetic tree is computationally prohibitive for large data sets. In this paper, we describe a new algorithm that uses Structural Expectation Maximization (EM) for learning maximum likelihood phylogenetic trees. This algorithm is similar to the standard EM method for edge-length estimation, except that during iterations of the Structural EM algorithm the topology is improved as well as the edge length. Our algorithm performs iterations of two steps. In the E-step, we use the current tree topology and edge 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. We show that searching for better topologies inside the M-step can be done efficiently, as opposed to standard methods for topology search. We prove that each iteration of this procedure increases the likelihood of the topology, and thus the procedure must converge. This convergence point, however, can be a suboptimal one. To escape from such "local optima," we further enhance our basic EM procedure by incorporating moves in the flavor of simulated annealing. We evaluate these new algorithms on both synthetic and real sequence data and show that for protein sequences even our basic algorithm finds more plausible trees than existing methods for searching maximum likelihood phylogenies. Furthermore, our algorithms are dramatically faster than such methods, enabling, for the first time, phylogenetic analysis of large protein data sets in the maximum likelihood framework.
Context-Specific Bayesian Clustering for Gene Expression Data

The recent growth in genomic data and measurements of genome-wide expression patterns allows us to apply computational tools to examine gene regulation by transcription factors. 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. Such a model represents the joint distribution of transcription factor binding sites and of expression levels of a gene in a unified probabilistic 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 life data and analyze the biological insights it provides. Finally, we demonstrate the applicability of the method to other data analysis problems in gene expression data.
A branch-and-bound algorithm for the inference of ancestral amino-acid sequences when the replacement rate varies among sites: Application to the evolution of five gene families

We developed an algorithm to reconstruct ancestral sequences, taking into account the rate variation among sites of the protein sequences. Our algorithm maximizes the joint probability of the ancestral sequences, assuming that the rate is gamma distributed among sites. Our algorithm probably finds the global maximum. The use of 'joint' reconstruction is motivated by studies that use the sequences at all the internal nodes in a phylogenetic tree, such as, for instance, the inference of patterns of amino-acid replacement, or tracing the biochemical changes that occurred during the evolution of a given protein family.



We give an algorithm that guarantees finding the global maximum. The efficient search method makes our method applicable to datasets with large number sequences. We analyze ancestral sequences of five gene families, exploring the effect of the amount of among-site-rate-variation, and the degree of sequence divergence on the resulting ancestral states.



http://evolu3.ism.ac.jp/~tal/



tal@ism.ac.jp
Data perturbation for escaping local maxima in learning

Almost all machine learning algorithms--be they for regression, classification or density estimation--seek hypotheses that optimize a score on training data. In most interesting cases, however, full global optimization is not feasible and local search techniques are used to discover reasonable solutions. Unfortunately, the quality of the local maxima reached depends on initialization and is often weaker than the global maximum. In this paper, we present a simple approach for combining global search with local optimization to discover improved hypotheses in general machine learning problems. The main idea is to escape local maxima by perturbing the <i>training data</i> to create plausible new ascent directions, rather than perturbing hypotheses directly. Specifically, we consider example-reweighting strategies that are reminiscent of boosting and other ensemble learning methods, but applied in a different way with a different goal: to produce a <i>single</i> hypothesis that achieves a good score on training and test data. To evaluate the performance of our algorithms we consider a number of problems in learning Bayesian networks from data, including discrete training problems (structure search), continuous training problems (parametric EM, non-linear logistic regression), and mixed training problems (Structural EM)- on both synthetic and real-world data. In each case, we obtain state of the art performance on both training and test data.
Statistical methods for analyzing gene expression data for cancer research.

Microarray technology allows for the simultaneous profiling of the expression levels of tens of thousands of genes, potentially whole genomes (DeRisi et al. 1997; Lipshutz et al. 1999; Shalon et al. 1996). This unique power has opened the way for new experimental designs that explore the transcriptional profile of cancer and other complex diseases (Alon et al. 1999; Golub et al. 1999; Perou et al. 1999; Schummer et al. 1999; Alizadeh et al. 2000; Bittner et al. 2000; Perou et al. 2000; Ross et al. 2000).
Practical approaches to analyzing results of microarray experiments.

Microarray technology is rapidly becoming a standard laboratory technique. The main challenges related to the successful implementation of the technology are analysis-related. In this article we provide a practically oriented review focusing on methods for analysis of large-scale gene expression data in the research laboratory. We describe the various common clustering methods and outline our approach to using them. We discuss methods for scoring genes for their relevance, focusing on the statistical meaning of microarray results, especially with regard to the problem of multiple testing. We also deal with the problem of adding biologic meaning to the results of microarray experiments and describe advanced tools that represent different but valid directions in providing automated solutions to this problem. The tools and approaches described and discussed here should provide the reader with a preliminary understanding of the analysis of the results of microarray experiments. The practical focus of this review should remove the mystery behind the analysis of microarray experiments, thus leading to more productive and efficient use of the technology.
Learning the Dimensionality of Hidden Variables

A serious problem in learning probabilistic models is the presence of hidden variables. These variables are not observed, yet interact with several of the observed variables. Detecting hidden variables poses two problems: determining the relations to other variables in the model and determining the number of states of the hidden variable. In this paper, we address the latter problem in the context of Bayesian networks. We describe an approach that utilizes a score-based agglomerative state-clustering. As we show, this approach allows us to efficiently evaluate models with a range of cardinalities for the hidden variable. We show how to extend this procedure to deal with multiple interacting hidden variables. We demonstrate the effectiveness of this approach by evaluating it on synthetic and real-life data. We show that our approach learns models with hidden variables that generalize better and have better structure than previous approaches.