Jie Zhang, Jiaxiang Zhao^*, Pengchang Xu

CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.2, pp. 905-917, 2022, DOI:10.32604/cmes.2022.019097

Abstract Accurately, reliably and rapidly identifying intrinsically disordered (IDPs) proteins is essential as they often play important roles in various human diseases; moreover, they are related to numerous important biological activities. However, current computational methods have yet to develop a network that is sufficiently deep to make predictions about IDPs and demonstrate an improvement in performance. During this study, we constructed a deep neural network that consisted of five identical variant models, ResNet18, combined with an MLP network, for classification. Resnet18 was applied for the first time as a deep model for predicting IDPs, which allowed the extraction of information from… More >

Jia Yang¹, Haiyuan Liu^1,*, Hao He²

CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.1, pp. 111-123, 2020, DOI:10.32604/cmes.2020.010347

Abstract The prediction of intrinsically disordered proteins is a hot research area in bio-information. Due to the high cost of experimental methods to evaluate disordered regions of protein sequences, it is becoming increasingly important to predict those regions through computational methods. In this paper, we developed a novel scheme by employing sequence complexity to calculate six features for each residue of a protein sequence, which includes the Shannon entropy, the topological entropy, the sample entropy and three amino acid preferences including Remark 465, Deleage/Roux, and Bfactor(2STD). Particularly, we introduced the sample entropy for calculating time series complexity by mapping the amino… More >