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Using Bayesian Networks to Analyze Expression Data
We propose a new framework for recovering gene interactions from microarray data using tools for learning Bayesian networks. Expand
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Learning Bayesian Network Structure from Massive Datasets: The "Sparse Candidate" Algorithm
Learning Bayesian networks is often cast as an optimization problem, where the computational task is to find a structure that maximizes a statistically motivated score. Expand
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Tissue Classification with Gene Expression Profiles
We present results of performing leave-one-out cross validation (LOOCV) experiments on three sets of gene expression data measured across sets of tumor(s) and normal clinical samples, employing nearest neighbor classifier, SVM (Cortes and Vapnik, 1995), AdaBoost (Freund and Schapire, 1997) and a novel clustering-based classification technique. Expand
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Tissue classification with gene expression profiles
We present results of performing leave-one-out cross validation (LOOCV) experiments on two sets of gene expression data measured across sets of tumor and normal clinical samples, using sets of selected genes, with as well as without cellular contamination related members. Expand
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Dynamic single-cell imaging of direct reprogramming reveals an early specifying event
The study of induced pluripotency often relies on experimental approaches that average measurements across a large population of cells, the majority of which do not become pluripotent. Here we usedExpand
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Inferring quantitative models of regulatory networks from expression data
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. Expand
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Gaussian Process Networks
We present a new family of continuous variable probabilistic networks that are based on Gaussian Process priors and describe how to learn them from data. Expand
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Dissecting Timing Variability in Yeast Meiosis
Cell-to-cell variability in the timing of cell-fate changes can be advantageous for a population of single-celled organisms growing in a fluctuating environment. We study timing variability duringExpand
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"Ideal Parent" Structure Learning for Continuous Variable Bayesian Networks
In this work we present a general method for speeding structure search for continuous variable networks with common parametric distributions. Expand
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Using Bayesian networks to analyze expression data
We propose a new framework for discovering interactions between genes based on multiple expression measurements, based on the use of Bayesian networks. Expand
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