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Empirical comparison of web-based antimicrobial peptide prediction tools
Author(s) -
Musa Gabere,
William Stafford Noble
Publication year - 2017
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btx081
Subject(s) - antimicrobial peptides , benchmark (surveying) , bacteriocin , computer science , machine learning , antimicrobial , artificial intelligence , support vector machine , predictive modelling , antibacterial peptide , data mining , biology , bacteria , microbiology and biotechnology , antibacterial activity , genetics , geodesy , geography
Antimicrobial peptides (AMPs) are innate immune molecules that exhibit activities against a range of microbes, including bacteria, fungi, viruses and protozoa. Recent increases in microbial resistance against current drugs has led to a concomitant increase in the need for novel antimicrobial agents. Over the last decade, a number of AMP prediction tools have been designed and made freely available online. These AMP prediction tools show potential to discriminate AMPs from non-AMPs, but the relative quality of the predictions produced by the various tools is difficult to quantify.

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