datPAV--an online processing, analysis and visualization tool for exploratory investigation of experimental data.

Summary: Data processing, analysis and visualization (datPAV) is an exploratory tool that allows experimentalist to quickly assess the general characteristics of the data. This platform-independent software is designed as a generic tool to process and visualize data matrices. This tool explores organization of the data, detect errors and support basic statistical analyses.

MetDAT: a modular and workflow-based free online pipeline for mass spectrometry data processing, analysis and interpretation.

Summary: Analysis of high throughput metabolomics experiments is a resource-intensive process that includes pre-processing, pre-treatment and post-processing at each level of experimental hierarchy. We developed an interactive user-friendly online software called Metabolite Data Analysis Tool (MetDAT) for mass spectrometry data. It offers a pipeline of tools for file handling, data pre-processing, univariate and multivariate statistical analyses, database searching and pathway mapping.

Amino-acid residue association models for large scale protein-protein interaction prediction.

Unlabelled: The computational prediction of protein-protein interactions (PPI) is an essential complement to direct experimental evidence. Traditional approaches rely on less available or computationally predicted surface properties, show database-specific performances and are computationally expensive for large-scale datasets. Several sensitivity and specificity issues remain.

Rich can get poor: conversion of hub to non-hub proteins.

Hubs are ubiquitous network elements with high connectivity. One of the common observations about hub proteins is their preferential attachment leading to scale-free network topology. Here we examine the question: does rich protein always get richer, or can it get poor too? To answer this question, we compared similar and well-annotated hub proteins in six organisms, from prokaryotes to eukaryotes.

Data-driven optimization of metabolomics methods using rat liver samples.

The aim of metabolomics is to identify, measure, and interpret complex time-related concentration, activity, and flux of metabolites in cells, tissues, and biofluids. We have used a metabolomics approach to study the biochemical phenotype of mammalian cells which will help in the development of a panel of early stage biomarkers of heat stress tolerance and adaptation. As a first step, a simple and sensitive mass spectrometry experimental workflow has been optimized for the profiling of metabolites in rat tissues.

Variable predictive model based classification algorithm for effective separation of protein structural classes.

Variable predictive model based class discrimination (VPMCD) algorithm is proposed as an effective protein secondary structure classification tool. The algorithm mathematically represents the characteristics amino acid interactions specific to each protein structure and exploits them further to distinguish different structures. The new concept and the VPMCD classifier are established using well-studied datasets containing four protein classes as benchmark.

Variable interaction network based variable selection for multivariate calibration.

Multivariate calibration problems often involve the identification of a meaningful subset of variables, from a vast number of variables for better prediction of output variables. A new graph theoretic method based on partial correlations (variable interaction network-VIN) is proposed. Many well studied representative calibration datasets spanning different application domains are selected for investigating the performance.

VPMCD: variable interaction modeling approach for class discrimination in biological systems.

Data classification algorithms applied for class prediction in computational biology literature are data specific and have shown varying degrees of performance. Different classes cannot be distinguished solely based on interclass distances or decision boundaries. We propose that inter-relations among the features be exploited for separating observations into specific classes.