MOTIVATION: Significant improvements in the prediction of protein residue-residue contacts are observed in the recent years. These contacts, predicted using a variety of coevolution-based and machine learning methods, are the key contributors to the ...
MOTIVATION: For in-depth understanding the functions of proteins in a cell, the knowledge of their subcellular localization is indispensable. The current study is focused on human protein subcellular location prediction based on the sequence informat...
We present a rationale for expanding the presence of the Lisp family of programming languages in bioinformatics and computational biology research. Put simply, Lisp-family languages enable programmers to more quickly write programs that run faster th...
Protein secondary structure prediction began in 1951 when Pauling and Corey predicted helical and sheet conformations for protein polypeptide backbone even before the first protein structure was determined. Sixty-five years later, powerful new method...
MOTIVATION: Protein fold recognition is an important problem in structural bioinformatics. Almost all traditional fold recognition methods use sequence (homology) comparison to indirectly predict the fold of a target protein based on the fold of a te...
MOTIVATION: Gene Ontology (GO) terms are frequently used to score alignments between protein-protein interaction (PPI) networks. Methods exist to measure GO similarity between proteins in isolation, but proteins in a network alignment are not isolate...
MOTIVATION: Reactome is a free, open-source, open-data, curated and peer-reviewed knowledgebase of biomolecular pathways. For web-based pathway visualization, Reactome uses a custom pathway diagram viewer that has been evolved over the past years. He...
MOTIVATION: Inferring the structure of gene regulatory networks from high-throughput datasets remains an important and unsolved problem. Current methods are hampered by problems such as noise, low sample size, and incomplete characterizations of regu...
MOTIVATION: Finding computationally drug-target interactions (DTIs) is a convenient strategy to identify new DTIs at low cost with reasonable accuracy. However, the current DTI prediction methods suffer the high false positive prediction rate.
MOTIVATION: The ever-increasing number of biomedical datasets provides tremendous opportunities for re-use but current data repositories provide limited means of exploration apart from text-based search. Ontological metadata annotations provide conte...
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