Genomics, epigenomics, transcriptomics, proteomics and metabolomics efforts rapidly
generate a plethora of data on the activity and levels of biomolecules within mammalian …

Background System-wide profiling of genes and proteins in mammalian cells produce lists of
differentially expressed genes/proteins that need to be further analyzed for their collective …

Data sets from recent large-scale projects can be integrated into one unified puzzle that can
provide new insights into how drugs and genetic perturbations applied to human cells are …

Abstract For the Library of Integrated Network-based Cellular Signatures (LINCS) project
many gene expression signatures using the L1000 technology have been produced. The …

Abstract Motivation: Adverse drug reactions (ADRs) are a central consideration during drug
development. Here we present a machine learning classifier to prioritize ADRs for approved …

High content studies that profile mouse and human embryonic stem cells (m/hESCs) using
various genome-wide technologies such as transcriptomics and proteomics are constantly …

Protein–protein interaction is of primary importance to understand protein functions. In
recent years, the high-throughput AP-MS experiments have generated a large amount of …

Research on hydrogen production by dark fermentation has improved the management and
performance of bioreactors, although yield and long-term stability challenges still exist. To …

Motivation: Networks are vital to computational systems biology research, but visualizing
them is a challenge. For networks larger than∼ 100 nodes and∼ 200 links, ball-and-stick …

Exponential-family random graph models (ERGMs) are probabilistic network models that are
parametrized by sufficient statistics based on structural (ie, graph-theoretic) properties. The …