Predicting Sequence-Activity Relationships among Antimicrobial Peptides
نویسندگان
چکیده
Antimicrobial peptides (AMPs) are membraneactive peptides that serve as promising therapeutic alternatives to antibiotics. Due to their diverse sequences, AMPs are active against a variety of microbes including bacteria, fungi, viruses, and cancer cells. Here, we present the use of machine learning techniques to classify antifungal AMPs from antibacterial AMPs using sequence-derived physico-chemical features. Although our best models performed moderately well with 70-75% accuracy, we attempt to rationalize the high variance using feature reduction techniques.
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