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• W1590965573 abstract "Rapid advances in high-throughput data acquisition technologies, such as microarrays and next-generation sequencing, have enabled the scientists to interrogate the expression levels of tens of thousands of genes simultaneously. However, challenges remain in developing effective computational methods for analyzing data generated from such platforms. In this dissertation, we address some of these challenges. We divide our work into two parts. In the first part, we present a suite of multivariate approaches for a reliable discovery of gene clusters, often interpreted as pathway components, from molecular profiling data with replicated measurements. We translate our goal into learning an optimal correlation structure from replicated complete and incomplete measurements. In the second part, we focus on the reconstruction of signal transduction mechanisms in the signaling pathway components. We propose gene set based approaches for inferring the structure of a signaling pathway. First, we present a constrained multivariate Gaussian model, referred to as the informed-case model, for estimating the correlation structure from replicated and complete molecular profiling data. Informed-case model generalizes previously known blind-case model by accommodating prior knowledge of replication mechanisms. Second, we generalize the blind-case model by designing a two-component mixture model. Our idea is to strike an optimal balance between a fully constrained correlation structure and an unconstrained one. Third, we develop an Expectation-Maximization algorithm to infer the underlying correlation structure from replicated molecular profiling data with missing (incomplete) measurements. We utilize our correlation estimators for clustering real-world replicated complete and incomplete molecular profiling data sets. The above three components constitute the first part of the dissertation. For the structural inference of signaling pathways, we hypothesize a directed signal pathway structure as an ensemble of overlapping and linear signal transduction events. We then propose two algorithms to reverse engineer the underlying signaling pathway structure using unordered gene sets corresponding to signal transduction events. Throughout we treat gene sets as variables and the associated gene orderings as random. The first algorithm has been developed under the Gibbs sampling framework and the second algorithm utilizes the framework of simulated annealing. Finally, we summarize our findings and discuss possible future directions. Keywords: Replicated data, incomplete data, correlation, covariance matrix, multivariate Gaussian mixture models, expectation-maximization (EM) algorithm, gene sets, Gibbs sampling, signaling pathways, signal transduction, discrete optimization, simulated annealing." @default.
• W1590965573 created "2016-06-24" @default.
• W1590965573 creator A5009256505 @default.
• W1590965573 creator A5087108082 @default.
• W1590965573 date "2011-01-01" @default.
• W1590965573 modified "2023-10-16" @default.
• W1590965573 title "Multivariate models and algorithms for systems biology" @default.
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