Publication: NeuroPred-FRL: an interpretable prediction model for identifying neuropeptide using feature representation learning
Issued Date
2021-11-05
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ISSN
14774054
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2-s2.0-85108972351
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Mahidol University
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SCOPUS
Bibliographic Citation
Briefings in bioinformatics. Vol.22, No.6 (2021)
Suggested Citation
Md Mehedi Hasan, Md Ashad Alam, Watshara Shoombuatong, Hong Wen Deng, Balachandran Manavalan, Hiroyuki Kurata NeuroPred-FRL: an interpretable prediction model for identifying neuropeptide using feature representation learning. Briefings in bioinformatics. Vol.22, No.6 (2021). doi:10.1093/bib/bbab167 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/75960
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Title
NeuroPred-FRL: an interpretable prediction model for identifying neuropeptide using feature representation learning
Abstract
Neuropeptides (NPs) are the most versatile neurotransmitters in the immune systems that regulate various central anxious hormones. An efficient and effective bioinformatics tool for rapid and accurate large-scale identification of NPs is critical in immunoinformatics, which is indispensable for basic research and drug development. Although a few NP prediction tools have been developed, it is mandatory to improve their NPs' prediction performances. In this study, we have developed a machine learning-based meta-predictor called NeuroPred-FRL by employing the feature representation learning approach. First, we generated 66 optimal baseline models by employing 11 different encodings, six different classifiers and a two-step feature selection approach. The predicted probability scores of NPs based on the 66 baseline models were combined to be deemed as the input feature vector. Second, in order to enhance the feature representation ability, we applied the two-step feature selection approach to optimize the 66-D probability feature vector and then inputted the optimal one into a random forest classifier for the final meta-model (NeuroPred-FRL) construction. Benchmarking experiments based on both cross-validation and independent tests indicate that the NeuroPred-FRL achieves a superior prediction performance of NPs compared with the other state-of-the-art predictors. We believe that the proposed NeuroPred-FRL can serve as a powerful tool for large-scale identification of NPs, facilitating the characterization of their functional mechanisms and expediting their applications in clinical therapy. Moreover, we interpreted some model mechanisms of NeuroPred-FRL by leveraging the robust SHapley Additive exPlanation algorithm.