计算机科学
深度学习
人工智能
水准点(测量)
多任务学习
图形
机器学习
超参数优化
卷积神经网络
网格
任务(项目管理)
模式识别(心理学)
理论计算机科学
数学
工程类
大地测量学
支持向量机
几何学
系统工程
地理
作者
Yasin Görmez,Zafer Aydın
标识
DOI:10.1109/tcbb.2022.3191395
摘要
Protein secondary structure, solvent accessibility and torsion angle predictions are preliminary steps to predict 3D structure of a protein. Deep learning approaches have achieved significant improvements in predicting various features of protein structure. In this study, IGPRED-Multitask, a deep learning model with multi task learning architecture based on deep inception network, graph convolutional network and a bidirectional long short-term memory is proposed. Moreover, hyper-parameters of the model are fine-tuned using Bayesian optimization, which is faster and more effective than grid search. The same benchmark test data sets as in the OPUS-TASS paper including TEST2016, TEST2018, CASP12, CASP13, CASPFM, HARD68, CAMEO93, CAMEO93_HARD, as well as the train and validation sets, are used for fair comparison with the literature. Statistically significant improvements are observed in secondary structure prediction on 4 datasets, in phi angle prediction on 2 datasets and in psi angel prediction on 3 datasets compared to the state-of-the-art methods. For solvent accessibility prediction, TEST2016 and TEST2018 datasets are used only to assess the performance of the proposed model.
科研通智能强力驱动
Strongly Powered by AbleSci AI