Integrative computational biology for cancer research

Over the past two decades, high-throughput (HTP) technologies such as microarrays and mass spectrometry have fundamentally changed clinical cancer research. They have revealed novel molecular markers of cancer subtypes, metastasis, and drug sensitivity and resistance. Some have been translated into the clinic as tools for early disease diagnosis, prognosis, and individualized treatment and response monitoring. Despite these successes, many challenges remain: HTP platforms are often noisy and suffer from false positives and false negatives; optimal analysis and successful validation require complex workflows; and great volumes of data are accumulating at a rapid pace. Here we discuss these challenges, and show how integrative computational biology can help diminish them by creating new software tools, analytical methods, and data standards

BioPAX-Parser: parsing and enrichment analysis of BioPAX pathways

Abstract Summary Biological pathways are fundamental for learning about healthy and disease states. Many existing formats support automatic software analysis of biological pathways, e.g. BioPAX (Biological Pathway Exchange). Although some algorithms are available as web application or stand-alone tools, no general graphical application for the parsing of BioPAX pathway data exists. Also, very few tools can perform pathway enrichment analysis (PEA) using pathway encoded in the BioPAX format. To fill this gap, we introduce BiP (BioPAX-Parser), an automatic and graphical software tool aimed at performing the parsing and accessing of BioPAX pathway data, along with PEA by using information coming from pathways encoded in BioPAX. Availability and implementation BiP is freely available for academic and non-profit organizations at https://gitlab.com/giuseppeagapito/bip under the LGPL 2.1, the GNU Lesser General Public License. Supplementary information Supplementary data are available at Bioinformatics online

Unequal evolutionary conservation of human protein interactions in interologous networks

The conservation of protein-protein interaction networks can be examined by mapping human proteins to yeast and other model organisms, revealing that protein complexes are preferentially conserved, and that such conservation can yield biological insights

Inferring the functions of longevity genes with modular subnetwork biomarkers of Caenorhabditis elegans aging

An algorithm for determining networks from gene expression data enables the identification of genes potentially linked to aging in worms