Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/143870
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dc.contributor.authorLe, Nguyen Quoc Khanhen_US
dc.contributor.authorHuynh, Tuan-Tuen_US
dc.contributor.authorYapp, Edward Kien Yeeen_US
dc.contributor.authorYeh, Hui-Yuanen_US
dc.date.accessioned2020-09-28T06:58:06Z-
dc.date.available2020-09-28T06:58:06Z-
dc.date.issued2019-
dc.identifier.citationLe, N. Q. K., Huynh, T.-T., Yapp, E. K. Y., & Yeh, H.-Y. (2019). Identification of clathrin proteins by incorporating hyperparameter optimization in deep learning and PSSM profiles. Computer Methods and Programs in Biomedicine, 177, 81–88. doi:10.1016/j.cmpb.2019.05.016en_US
dc.identifier.issn0169-2607en_US
dc.identifier.urihttps://hdl.handle.net/10356/143870-
dc.description.abstractBackground and Objectives: Clathrin is an adaptor protein that serves as the principal element of the vesicle-coating complex and is important for the membrane cleavage to dispense the invaginated vesicle from the plasma membrane. The functional loss of clathrins has been tied to a lot of human diseases, i.e., neurodegenerative disorders, cancer, Alzheimer's diseases, and so on. Therefore, creating a precise model to identify its functions is a crucial step towards understanding human diseases and designing drug targets. Methods:We present a deep learning model using a two-dimensional convolutional neural network (CNN) and position-specific scoring matrix (PSSM) profiles to identify clathrin proteins from high throughput sequences. Traditionally, the 2D CNNs take images as an input so we treated the PSSM profile with a 20 × 20 matrix as an image of 20 × 20 pixels. The input PSSM profile was then connected to our 2D CNN in which we set a variety of parameters to improve the performance of the model. Based on the 10-fold cross-validation results, hyper-parameter optimization process was employed to find the best model for our dataset. Finally, an independent dataset was used to assess the predictive ability of the current model.Results:Our model could identify clathrin proteins with sensitivity of 92.2%, specificity of 91.2%, accuracy of 91.8%, and MCC of 0.83 in the independent dataset. Compared to state-of-the-art traditional neural networks, our method achieved a significant improvement in all typical measurement metrics. Conclusions:Throughout the proposed study, we provide an effective tool for investigating clathrin proteins and our achievement could promote the use of deep learning in biomedical research. We also provide source codes and dataset freely at https://www.github.com/khanhlee/deep-clathrin/.en_US
dc.language.isoenen_US
dc.relation.ispartofComputer methods and programs in biomedicineen_US
dc.rights© 2019 Elsevier B.V. All rights reserved. This paper was published in Computer Methods and Programs in Biomedicine and is made available with permission of Elsevier B.V.en_US
dc.subjectHumanities::Generalen_US
dc.titleIdentification of clathrin proteins by incorporating hyperparameter optimization in deep learning and PSSM profilesen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Humanitiesen_US
dc.identifier.doi10.1016/j.cmpb.2019.05.016-
dc.description.versionAccepted versionen_US
dc.identifier.pmid31319963-
dc.identifier.volume177en_US
dc.identifier.spage81en_US
dc.identifier.epage88en_US
dc.subject.keywordsClathrin Coated Pitsen_US
dc.subject.keywordsConvolutional Neural Networken_US
item.fulltextWith Fulltext-
item.grantfulltextopen-
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