Results 1  10
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21
Algorithms for Large Scale Markov Blanket Discovery
 In The 16th International FLAIRS Conference, St
, 2003
"... This paper presents a number of new algorithras for discovering the Markov Blanket of a target variable T from training data. The Markov Blanket can be used for variable selection for classification, for causal discovery, and for Bayesian Network learning. We introduce a loworder polynomial algorit ..."
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Cited by 28 (4 self)
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This paper presents a number of new algorithras for discovering the Markov Blanket of a target variable T from training data. The Markov Blanket can be used for variable selection for classification, for causal discovery, and for Bayesian Network learning. We introduce a loworder polynomial algorithm and several variants that soundly induce the Markov Blanket under certain broad conditions in datasets with thousands of variables and compare them to other stateoftheart local and global methods with excellent results.
Inference of transcriptional regulation relationships with gene expression data
, 2002
"... Motivation: In order to find gene regulatory networks from microarray data, it is important to first find direct regulatory relationships between pairs of genes. Results: We propose a new method for finding potential regulatory relationships between pairs of genes from microarray time series data an ..."
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Cited by 19 (0 self)
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Motivation: In order to find gene regulatory networks from microarray data, it is important to first find direct regulatory relationships between pairs of genes. Results: We propose a new method for finding potential regulatory relationships between pairs of genes from microarray time series data and apply it to expression data for cellcycle related genes in yeast. We compare our algorithm, dubbed the event method, with the earlier correlation method and the edge detection method by Filkov et al. When tested on known transcriptional regulation genes, all three methods are able to find similar numbers of true positives. The results indicate that our algorithm is able to identify true positive pairs that are different from those found by the two other methods. We also compare the correlation and the event methods using synthetic data and find that typically, the event method obtains better results. Availability: Software is available upon request. Contact:
Learning Bayes net structure from sparse data sets
, 2001
"... There are essentially two kinds of approaches for learning the structure of Bayesian Networks (BNs) from data. The first approach tries to find a graph which satis es all the constraints implied by the empirical conditional independencies measured in the data [PV91, SGS00a, Shi00]. The second approa ..."
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Cited by 12 (2 self)
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There are essentially two kinds of approaches for learning the structure of Bayesian Networks (BNs) from data. The first approach tries to find a graph which satis es all the constraints implied by the empirical conditional independencies measured in the data [PV91, SGS00a, Shi00]. The second approach searches through the space of models (either DAGs or PDAGs), and uses some scoring metric (typically Bayesian or some approximation, such as BIC/MDL) to evaluate the models [CH92, Hec95, Hec98, Kra98], typically returning the highest scoring model found. Our main interest is in learning BN structure from gene expression data [FLNP00, HGJY01, MM99, SGS00b]. In domains such as this, where the ratio of the number of observations to the number of variables is low (i.e., when we have sparse data), selecting a threshold for the conditional independence (CI) tests can be tricky, and repeated use of such tests can lead to inconsistencies [DD99]. Bayesian s...
N1 experiments suffice to determine the causal relations among N variables
 In Innovations in Machine Learning
, 2006
"... By combining experimental interventions with search procedures for graphical causal models we show that under familiar assumptions, with perfect data, N 1 experiments suffice to determine the causal relations among N>2 variables when each experiment randomizes at most one variable. We show the same ..."
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Cited by 8 (4 self)
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By combining experimental interventions with search procedures for graphical causal models we show that under familiar assumptions, with perfect data, N 1 experiments suffice to determine the causal relations among N>2 variables when each experiment randomizes at most one variable. We show the same bound holds for adaptive learners, but does not hold for N> 4 when each experiment can simultaneously randomize more than one variable. This bound provides a type of ideal for the measure of success of heuristic approaches in active learning methods of causal discovery, which currently use less informative measures. Three Methods and Their Limitations Consider situations in which the aim of inquiry is to determine the causal structure of a kind of system with many variables, for example the gene regulation network of a species in a particular environment. The aim in other words is to determine for each pair X, Y of variables in a set of variables, S, whether X directly causes Y (or viceversa), with respect to the remaining variables in S, i.e., for some assignment of values V to all the remaining
An evaluation of a system that recommends microarray experiments to perform to discover generegulation pathways
 Journal Artificial Intelligence in Medicine
, 2003
"... The main topic of this paper is modeling the expected value of experimentation for discovering causal pathways in gene expression data. By experimentation we mean both interventions (e.g., a gene knockout experiment) and observations (e.g., passively observing the expression level of a “wildtype ” ..."
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Cited by 7 (0 self)
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The main topic of this paper is modeling the expected value of experimentation for discovering causal pathways in gene expression data. By experimentation we mean both interventions (e.g., a gene knockout experiment) and observations (e.g., passively observing the expression level of a “wildtype ” gene). We introduce a system called GEEVE (causal discovery in Gene Expression data using Expected Value of Experimentation), which implements expected value of experimentation in discovering causal pathways using gene expression data. GEEVE provides the following assistance, which is intended to help biologists in their quest to discover generegulation pathways: • Recommending which experiments to perform (with a focus on “knockout ” experiments) using an expected value of experimentation (EVE) method. • Recommending the number of measurements (observational and experimental) to include in the experimental design, again using an EVE method. • Providing a Bayesian analysis that combines prior knowledge with the results of recent microarray experimental results to derive posterior probabilities of gene regulation
Systems biology via redescription and ontologies (II): A Tool for Discovery in Complex Systems
"... A complex system creates a “whole that is larger than the sum of its parts,” by coordinating many interacting simpler component processes. Yet, each of these processes is difficult to decipher as their visible signatures are only seen in a syntactic background, devoid of the context. Examples of suc ..."
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Cited by 7 (3 self)
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A complex system creates a “whole that is larger than the sum of its parts,” by coordinating many interacting simpler component processes. Yet, each of these processes is difficult to decipher as their visible signatures are only seen in a syntactic background, devoid of the context. Examples of such visible datasets are timecourse description of geneexpression abundance levels, neural spiketrains, or clickstreams for web pages. It has now become rather effortless to collect voluminous datasets of this nature; but how can we make sense of them and draw significant conclusions? For instance, in the case of timecourse geneexpression datasets, rather than following small sets of known genes, can we develop a holistic approach that provides a view of the entire system as it evolves through time? We have developed GOALIE (GeneOntology for Algorithmic Logic and Invariant Extraction) a systems biology application that presents global and dynamic perspectives (e.g., invariants) inferred collectively over a geneexpression dataset. Such perspectives are important in order to obtain a processlevel understanding of the underlying cellular machinery; especially how cells react, respond, and recover from
Inferring gene transcriptional modulatory relations: a genetical genomics approach
 Hum. Mol. Genet
, 2005
"... ..."
Studying the Conditions for Learning Dynamic Bayesian Networks to Discover Genetic Regulatory Networks
 SIMULATION
, 2003
"... On behalf of: ..."
Interventions and causal inference
 Philosophy of Science
, 2007
"... The literature on causal discovery has focused on interventions that involve randomly assigning values to a single variable. But such a randomized intervention is not the only possibility, nor is it always optimal. In some cases it is impossible or it would be unethical to perform such an interventi ..."
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Cited by 5 (1 self)
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The literature on causal discovery has focused on interventions that involve randomly assigning values to a single variable. But such a randomized intervention is not the only possibility, nor is it always optimal. In some cases it is impossible or it would be unethical to perform such an intervention. We provide an account of “hard ” and “soft” interventions, and discuss what they can contribute to causal discovery. We also describe how the choice of the optimal intervention(s) depends heavily on the particular experimental setup and the assumptions that can be made.
Bayesian Networks for Genomic Analysis
, 2004
"... Bayesian networks are emerging into the genomic arena as a general modeling tool able to unravel the cellular mechanism, to identify genotypes that confer susceptibility to disease, and to lead to diagnostic models. This chapter reviews the foundations of Bayesian networks and shows their applicatio ..."
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Cited by 2 (0 self)
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Bayesian networks are emerging into the genomic arena as a general modeling tool able to unravel the cellular mechanism, to identify genotypes that confer susceptibility to disease, and to lead to diagnostic models. This chapter reviews the foundations of Bayesian networks and shows their application to the analysis of various types of genomic data, from genomic markers to gene expression data. The examples will highlight the potential of this methodology but also the current limitations and we will describe new research directions that hold the promise to make Bayesian networks a fundamental tool for genome data