Publication: ProIn-Fuse: improved and robust prediction of proinflammatory peptides by fusing of multiple feature representations
Issued Date
2020-01-01
Resource Type
ISSN
15734951
0920654X
0920654X
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2-s2.0-85091282929
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Mahidol University
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SCOPUS
Bibliographic Citation
Journal of Computer-Aided Molecular Design. (2020)
Suggested Citation
Mst Shamima Khatun, Md Mehedi Hasan, Watshara Shoombuatong, Hiroyuki Kurata ProIn-Fuse: improved and robust prediction of proinflammatory peptides by fusing of multiple feature representations. Journal of Computer-Aided Molecular Design. (2020). doi:10.1007/s10822-020-00343-9 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/59038
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Title
ProIn-Fuse: improved and robust prediction of proinflammatory peptides by fusing of multiple feature representations
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Abstract
© 2020, Springer Nature Switzerland AG. A proinflammatory peptide (PIP) is a type of signaling molecules that are secreted from immune cells, which contributes to the first line of defense against invading pathogens. Numerous experiments have shown that PIPs play an important role in human physiology such as vaccines and immunotherapeutic drugs. Considering high-throughput laboratory methods that are time consuming and costly, effective computational methods are great demand to timely and accurately identify PIPs. Thus, in this study, we proposed a computational model in conjunction with a multiple feature representation, called ProIn-Fuse, to improve the performance of PIPs identification. Specifically, a feature representation learning model was utilized to generate the probabilistic scores by using the random forest models employing eight sequence encoding schemes. Finally, the ProIn-Fuse was constructed by linearly combining the resultant eight probabilistic scores. Evaluated through independent test, the ProIn-Fuse yielded an accuracy of 0.746, which was 10% higher than those obtained by the state-of-the-art PIP predictors. The proposed ProIn-Fuse can facilitate faster and broader applications of PIPs in drug design and development. The web server, datasets and online instruction are freely accessible at http://kurata14.bio.kyutech.ac.jp/ProIn-Fuse.