Modeling Cooperative Gene Regulation Using Fast Orthogonal Search

Abstract

A number of computational methods have suggested means by which gene transcription -the process through which RNA is created from DNA -is activated, but there are factors at work that no model has been able to fully explain. In eukaryotes, gene regulation is quite complex, so models have primarily focused on a relatively simple species, Saccharomyces cerevisiae (budding yeast). Because of the inherent complexity in higher species, and even in yeast, a method of identifying transcription factor (TF) binding motifs (specific, short DNA sequences) must be efficient and thorough in its analysis.This thesis shows that a method using the Fast Orthogonal Search (FOS) algorithm to uncover binding motifs as well as cooperatively binding groups of motifs can explain variations in gene expression profiles, which reflect the level at which DNA is transcribed into RNA for a number of genes. The algorithm is very fast, exploring a motif list and constructing a final model within seconds to a few minutes. It produces model terms that are consistent with known motifs, while also revealing new motifs and interactions, and it causes impressive reductions in variance with relatively few model terms over the cell-cycle.iii Co-authorshipPortions of this thesis were co-written along with Michael Korenberg for use in a presently unpublished paper.iv

Feature Selection in Computer-Aided Breast Cancer Diagnosis via Dynamic Contrast-Enhanced Magnetic Resonance Images

“…In addition to the original features derived from the DCE-MRI images, cross-products of the original features can be used as candidate features. Cross-products of features may have more predictive value than the original features and are thus worth testing in a feature selection algorithm [20,25].…”

“…FOS has been shown to be highly effective in selecting appropriate features to model biological applications [20,21]. Shirdel et al [21] used FOS to identify features that predict which patients are at high risk for neutropenia based on information collected in the first cycle of a six-cycle chemotherapy treatment.…”

“…Shirdel et al [21] used FOS to identify features that predict which patients are at high risk for neutropenia based on information collected in the first cycle of a six-cycle chemotherapy treatment. Minz and Korenberg [20] used FOS in the appropriate selection of motifs and interacting groups of motifs involved in gene regulation.…”

Fast orthogonal search for genetic feature selection

“…Korenberg et al demonstrated the ability of FOS to model temporal biological signals such as electroencephalogram and electromyogram data [6]. Minz et al [5]have implemented FOS on molecular data by searching the promoter regions of genes on the DNA to indentify motifs, hence build cooperative gene network models for the yeast cell-cycle [5]. Mostafavi feature selection and classification framework to predict the response of multiple sclerosis patients to therapy, from gene expression data [7].…”

A Primer on Regression Methods for Decoding cis-Regulatory Logic

“…During the preparation of this manuscript, a new regression approach based on the Fast Orthogonal Search (FOS) method [58] was published to identify active cis -regulatory elements. As new algorithms get published, we will continue to maintain an updated version of Table 1 on our Web site http://vision.lbl.gov/People/ddas/RegressionPrimer/ .…”