Download PDFOpen PDF in browserExploring Deep Neural Network Architectures: A Case Study on Improving Antimicrobial Peptide Recognition10 pages•Published: March 11, 2020AbstractWith antibiotic resistance on the rise, health organizations are urging for the design of new drug templates. Naturally-occurring antimicrobial peptides (AMPs) promise to serve as such templates, as they show lower likelihood for bacteria to form resistance. This has motivated wet and dry laboratories to seek novel AMPs. The sequence diversity of these peptides, however, renders systematic wet-lab screening studies either infeasible or too narrow in scope. Dry laboratories have focused instead on machine learning approaches. In this paper, we explore various deep neural network architectures aimed at improving antimicrobial peptide recognition. Our enquiry results in several architectures with com- parable or better performance than existing, state-of-the-art discriminative models.Keyphrases: antimicrobial peptide recognition, convolution layer, deep learning, recurrent layer In: Qin Ding, Oliver Eulenstein and Hisham Al-Mubaid (editors). Proceedings of the 12th International Conference on Bioinformatics and Computational Biology, vol 70, pages 182-191.
|
