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A Bi-LSTM Based Ensemble Algorithm for Prediction of Protein Secondary Structure
Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).ORCID-id: 0000-0002-7115-9751
Rekke forfattare: 42019 (engelsk)Inngår i: Applied Sciences, E-ISSN 2076-3417, Vol. 9, nr 17, artikkel-id 3538Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

The prediction of protein secondary structure continues to be an active area of research in bioinformatics. In this paper, a Bi-LSTM based ensemble model is developed for the prediction of protein secondary structure. The ensemble model with dual loss function consists of five sub-models, which are finally joined by a Bi-LSTM layer. In contrast to existing ensemble methods, which generally train each sub-model and then join them as a whole, this ensemble model and sub-models can be trained simultaneously and the performance of each model can be observed and compared during the training process. Three independent test sets (e.g., data1199, 513 protein Cuff & Barton set (CB513) and 203 proteins from Critical Appraisals Skills Programme (CASP203)) are employed to test the method. On average, the ensemble model achieved 84.3% in Q(3) accuracy and 81.9% in segment overlap measure (SOV) score by using 10-fold cross validation. There is an improvement of up to 1% over some state-of-the-art prediction methods of protein secondary structure.

sted, utgiver, år, opplag, sider
2019. Vol. 9, nr 17, artikkel-id 3538
Emneord [en]
protein secondary structure, sequence analysis, Bi-LSTM, ensemble algorithm, deep learning
HSV kategori
Identifikatorer
URN: urn:nbn:se:su:diva-175876DOI: 10.3390/app9173538ISI: 000488603600100OAI: oai:DiVA.org:su-175876DiVA, id: diva2:1374627
Tilgjengelig fra: 2019-12-02 Laget: 2019-12-02 Sist oppdatert: 2019-12-12bibliografisk kontrollert

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