A network flow model for biclustering via optimal re-ordering of data matrices

Biclustering Methods for Re-ordering Data Matrices in Systems Biology, Drug Discovery and Toxicology

A novel framework for predicting in vivo toxicities from in vitro data using optimal methods for dense and sparse matrix reordering and logistic regression.

In this work, we combine the strengths of mixed-integer linear optimization (MILP) and logistic regression for predicting the in vivo toxicity of chemicals using only their measured in vitro assay data. The proposed approach utilizes a biclustering method based on iterative optimal reordering (DiMaggio, P. A., McAllister, S. R., Floudas, C. A., Feng, X. J., Rabinowitz, J. D., and Rabitz, H. A. ...

OPTIMAL ANALYSIS OF NON-REGULAR GRAPHS USING THE RESULTS OF REGULAR MODELS VIA AN ITERATIVE METHOD

In this paper an efficient method is developed for the analysis of non-regular graphs which contain regular submodels. A model is called regular if it can be expressed as the product of two or three subgraphs. Efficient decomposition methods are available in the literature for the analysis of some classes of regular models. In the present method, for a non-regular model, first the nodes of the ...

OPTIMAL ANALYSIS OF NON-REGULAR GRAPHS USING THE RESULTS OF REGULAR MODELS VIA AN ITERATIVE METHOD

In this paper an efficient method is developed for the analysis of non-regular graphs which contain regular submodels. A model is called regular if it can be expressed as the product of two or three subgraphs. Efficient decomposition methods are available in the literature for the analysis of some classes of regular models. In the present method, for a non-regular model, first the nodes of th...

Gene co-expression networks via biclustering Differential gene co-expression networks via Bayesian biclustering models

Identifying latent structure in large data matrices is essential for exploring biological processes. Here, we consider recovering gene co-expression networks from gene expression data, where each network encodes relationships between genes that are locally co-regulated by shared biological mechanisms. To do this, we develop a Bayesian statistical model for biclustering to infer subsets of co-re...