iAMP-2L: a two-level multi-label classifier for identifying antimicrobial peptides and their functional types.

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APD3: the antimicrobial peptide database as a tool for research and education (2015, background): “(45) tested a twolevel multi-label classifier iAMP-2L based on the multiple functions of AMPs annotated in the APD2.”
Impacts of bioinformatics to medicinal chemistry. (2015, background): “Again, many AMPs belong to two or more functional types, and hence the multi-label approach is needed to investigate their functional types [46] as well.”
pSuc-Lys: Predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach. (2016, background): “For the multi-label systems whose emergence has become more frequent in system biology [75-77] and system medicine [78], a completely different set of metrics is needed as elaborated in [79].”
Strategies and molecular tools to fight antimicrobial resistance: resistome, transcriptome, and antimicrobial peptides (2013, background): “Peptides are promiscuous molecules (Franco, 2011) and are invariably classified in more than one class and family (Xiao et al., 2013).”
Deep learning improves antimicrobial peptide recognition (2018, methodology): “Recent ones include iAMPpred (Meher et al. , 2017), iAMP-2L (Xiao et al. , 2013), AntiBP2 (Lata et al. , 2010), CAMP (Thomas et al. , 2009) and AMPer (Fjell et al. , 2007).”