Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/150969
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dc.contributor.authorLe, Nguyen Quoc Khanhen_US
dc.contributor.authorYapp, Edward Kien Yeeen_US
dc.contributor.authorOu, Yu-Yenen_US
dc.contributor.authorYeh, Hui-Yuanen_US
dc.date.accessioned2021-05-31T08:27:10Z-
dc.date.available2021-05-31T08:27:10Z-
dc.date.issued2019-
dc.identifier.citationLe, N. Q. K., Yapp, E. K. Y., Ou, Y. & Yeh, H. (2019). iMotor-CNN : identifying molecular functions of cytoskeleton motor proteins using 2D convolutional neural network via Chou's 5-step rule. Analytical Biochemistry, 575, 17-26. https://dx.doi.org/10.1016/j.ab.2019.03.017en_US
dc.identifier.issn0003-2697en_US
dc.identifier.urihttps://hdl.handle.net/10356/150969-
dc.description.abstractMotor proteins are the driving force behind muscle contraction and are responsible for the active transportation of most proteins and vesicles in the cytoplasm. There are three superfamilies of cytoskeletal motor proteins with various molecular functions and structures: dynein, kinesin, and myosin. The functional loss of a specific motor protein molecular function has linked to a variety of human diseases, e.g., Charcot-Marie-Tooth disease, kidney disease. Therefore, creating a precise model to classify motor proteins is essential for helping biologists understand their molecular functions and design drug targets according to their impact on human diseases. Here we attempt to classify cytoskeleton motor proteins using deep learning, which has been increasingly and widely used to address numerous problems in a variety of fields resulting in state-of-the-art results. Our effective deep convolutional neural network is able to achieve an independent test accuracy of 97.5%, 96.4%, and 96.1% for each superfamily, respectively. Compared to other state-of-the-art methods, our approach showed a significant improvement in performance across a range of evaluation metrics. Through the proposed study, we provide an effective model for classifying motor proteins and a basis for further research that can enhance the performance of protein function classification using deep learning.en_US
dc.description.sponsorshipNanyang Technological Universityen_US
dc.language.isoenen_US
dc.relation.ispartofAnalytical Biochemistryen_US
dc.rights© 2019 Elsevier Inc. All rights reserved.en_US
dc.subjectScience::Biological sciencesen_US
dc.titleiMotor-CNN : identifying molecular functions of cytoskeleton motor proteins using 2D convolutional neural network via Chou's 5-step ruleen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Humanitiesen_US
dc.identifier.doi10.1016/j.ab.2019.03.017-
dc.identifier.pmid30930199-
dc.identifier.scopus2-s2.0-85063631665-
dc.identifier.volume575en_US
dc.identifier.spage17en_US
dc.identifier.epage26en_US
dc.subject.keywordsCytoskeletal Filamentsen_US
dc.subject.keywordsProtein Function Predictionen_US
dc.description.acknowledgementThis work has been supported by the Nanyang Technological University Start-Up Grant.en_US
item.grantfulltextnone-
item.fulltextNo Fulltext-
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