Meta-iPVP: a sequence-based meta-predictor for improving the prediction of phage virion proteins using effective feature representation

© 2020, Springer Nature Switzerland AG. Phage virion protein (PVP) perforate the host cell membrane and eventually culminates in cell rupture thereby releasing replicated phages. The accurate identification of PVP is thus a crucial step towards improving our understanding of the biological function...

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Main Authors: Phasit Charoenkwan, Chanin Nantasenamat, Md Mehedi Hasan, Watshara Shoombuatong
Format: Journal
Published: 2020
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http://cmuir.cmu.ac.th/jspui/handle/6653943832/70369
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Institution: Chiang Mai University
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spelling th-cmuir.6653943832-703692020-10-14T08:47:01Z Meta-iPVP: a sequence-based meta-predictor for improving the prediction of phage virion proteins using effective feature representation Phasit Charoenkwan Chanin Nantasenamat Md Mehedi Hasan Watshara Shoombuatong Chemistry Computer Science Pharmacology, Toxicology and Pharmaceutics © 2020, Springer Nature Switzerland AG. Phage virion protein (PVP) perforate the host cell membrane and eventually culminates in cell rupture thereby releasing replicated phages. The accurate identification of PVP is thus a crucial step towards improving our understanding of the biological function and mechanisms of PVPs. Therefore, it is desirable to develop a computational method that is capable of fast and accurate identification of PVPs. To address this, we propose a novel sequence-based meta-predictor employing probabilistic information (referred herein as the Meta-iPVP) for the accurate identification of PVPs. Particularly, efficient feature representation approach was used to generate discriminative probabilistic features from four machine learning (ML) algorithms making use of seven feature encodings. To the best of our knowledge, the Meta-iPVP is the first meta-based approach that has been developed for PVP prediction. Independent test results indicated that the Meta-iPVP could discern important characteristics between PVPs and non-PVPs as well as achieving the best accuracy and MCC of 0.817 and 0.642, respectively, which corresponds to 6–10% and 14–21% improvements over existing PVP predictors. As such, this demonstrates that the proposed Meta-iPVP is a more efficient, robust and promising for the identification of PVPs. The predictive model is deployed as a publicly accessible Meta-iPVP webserver freely available online at http://camt.pythonanywhere.com/Meta-iPVP. 2020-10-14T08:28:28Z 2020-10-14T08:28:28Z 2020-10-01 Journal 15734951 0920654X 2-s2.0-85086579754 10.1007/s10822-020-00323-z https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85086579754&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/70369
institution Chiang Mai University
building Chiang Mai University Library
continent Asia
country Thailand
Thailand
content_provider Chiang Mai University Library
collection CMU Intellectual Repository
topic Chemistry
Computer Science
Pharmacology, Toxicology and Pharmaceutics
spellingShingle Chemistry
Computer Science
Pharmacology, Toxicology and Pharmaceutics
Phasit Charoenkwan
Chanin Nantasenamat
Md Mehedi Hasan
Watshara Shoombuatong
Meta-iPVP: a sequence-based meta-predictor for improving the prediction of phage virion proteins using effective feature representation
description © 2020, Springer Nature Switzerland AG. Phage virion protein (PVP) perforate the host cell membrane and eventually culminates in cell rupture thereby releasing replicated phages. The accurate identification of PVP is thus a crucial step towards improving our understanding of the biological function and mechanisms of PVPs. Therefore, it is desirable to develop a computational method that is capable of fast and accurate identification of PVPs. To address this, we propose a novel sequence-based meta-predictor employing probabilistic information (referred herein as the Meta-iPVP) for the accurate identification of PVPs. Particularly, efficient feature representation approach was used to generate discriminative probabilistic features from four machine learning (ML) algorithms making use of seven feature encodings. To the best of our knowledge, the Meta-iPVP is the first meta-based approach that has been developed for PVP prediction. Independent test results indicated that the Meta-iPVP could discern important characteristics between PVPs and non-PVPs as well as achieving the best accuracy and MCC of 0.817 and 0.642, respectively, which corresponds to 6–10% and 14–21% improvements over existing PVP predictors. As such, this demonstrates that the proposed Meta-iPVP is a more efficient, robust and promising for the identification of PVPs. The predictive model is deployed as a publicly accessible Meta-iPVP webserver freely available online at http://camt.pythonanywhere.com/Meta-iPVP.
format Journal
author Phasit Charoenkwan
Chanin Nantasenamat
Md Mehedi Hasan
Watshara Shoombuatong
author_facet Phasit Charoenkwan
Chanin Nantasenamat
Md Mehedi Hasan
Watshara Shoombuatong
author_sort Phasit Charoenkwan
title Meta-iPVP: a sequence-based meta-predictor for improving the prediction of phage virion proteins using effective feature representation
title_short Meta-iPVP: a sequence-based meta-predictor for improving the prediction of phage virion proteins using effective feature representation
title_full Meta-iPVP: a sequence-based meta-predictor for improving the prediction of phage virion proteins using effective feature representation
title_fullStr Meta-iPVP: a sequence-based meta-predictor for improving the prediction of phage virion proteins using effective feature representation
title_full_unstemmed Meta-iPVP: a sequence-based meta-predictor for improving the prediction of phage virion proteins using effective feature representation
title_sort meta-ipvp: a sequence-based meta-predictor for improving the prediction of phage virion proteins using effective feature representation
publishDate 2020
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85086579754&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/70369
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