Protein network inference from multiple genomic data: a supervised approach.

MOTIVATION: An increasing number of observations support the hypothesis that most biological functions involve the interactions between many proteins, and that the complexity of living systems arises as a result of such interactions. In this context, the problem of inferring a global protein network for a given organism, using all available genomic data about the organism, is quickly becoming one of the main challenges in current computational biology. RESULTS: This paper presents a new method to infer protein networks from multiple types of genomic data. Based on a variant of kernel canonical correlation analysis, its originality is in the formalization of the protein network inference problem as a supervised learning problem, and in the integration of heterogeneous genomic data within this framework. We present promising results on the prediction of the protein network for the yeast Saccharomyces cerevisiae from four types of widely available data: gene expressions, protein interactions measured by yeast two-hybrid systems, protein localizations in the cell and protein phylogenetic profiles. The method is shown to outperform other unsupervised protein network inference methods. We finally conduct a comprehensive prediction of the protein network for all proteins of the yeast, which enables us to propose protein candidates for missing enzymes in a biosynthesis pathway. AVAILABILITY: Softwares are available upon request.

Extraction of correlated gene clusters from multiple genomic data by generalized kernel canonical correlation analysis.

MOTIVATION: A major issue in computational biology is the reconstruction of pathways from several genomic datasets, such as expression data, protein interaction data and phylogenetic profiles. As a first step toward this goal, it is important to investigate the amount of correlation which exists between these data. RESULTS: These methods are successfully tested on their ability to recognize operons in the Escherichia coli genome, from the comparison of three datasets corresponding to functional relationships between genes in metabolic pathways, geometrical relationships along the chromosome, and co-expression relationships as observed by gene expression data.

Support vector machine prediction of signal peptide cleavage site using a new class of kernels for strings.

A new class of kernels for strings is introduced. These kernels can be used by any kernel-based data analysis method, including support vector machines (SVM). They are derived from probabilistic models to integrate biologically relevant information. We show how to compute the kernels corresponding to several classical probabilistic models, and illustrate their use by building a SVM for the problem of predicting the cleavage site of signal peptides from the amino-acid sequence of a protein. At a given rate of false positive this method retrieves up to 47% more true positives than the classical weight matrix method.