Use Chou’s 5-Steps Rule to Predict Remote Homology Proteins by Merging Grey Incidence Analysis and Domain Similarity Analysis

Abstract

Detecting remote homology proteins is a challenging problem for both basic research and drug development. Although there are a couple of methods to deal with this problem, the benchmark datasets based on which the existing methods were trained and tested contain many high homologous samples as reflected by the fact that the cutoff threshold was set at 95%. In this study, we reconstructed the benchmark dataset by setting the threshold at 40%, meaning none of the proteins included in the benchmark dataset has more than 40% pairwise sequence identity with any other in the same subset. Using the new benchmark dataset, we proposed a new predictor called "dRHP-GreyFun" based on the grey modeling and functional domain approach. Rigorous cross-validations have indicated that the new predictor is superior to its counterparts in both enhancing success rates and reducing computational cost. The predictor can be downloaded from https://github.com/jcilwz/dRHP-GreyFun.

iDRP-PseAAC: Identification of DNA Replication Proteins Using General PseAAC and Position Dependent Features

“…This sequence-based statistical predictor operates based on the following five prime rules i.e. 5-step rule (Chou 2020a , b , c , d , e , f ; Fang et al 2020 ; Lin et al 2020 ; Liu and Chou 2020 ; Lu and Chou 2020 ; Shao and Chou 2020 ; Shao et al 2020 ; Xu et al 2020 ; Zhang et al 2020 ) which is (i) Construction or selection of an effective benchmark dataset for training and testing the sequence-based statistical predictor, (ii) Formulation of the effectual biological sequence tasters with an operative measured expression to accurately replicate the intrinsic relation of the biological sequence with the target to be prophesied, (iii) Development of a productive and efficacious algorithm for operating the prediction, (iv) Execution of persuasive cross-validation trials to factually assess the projected precision of the predictor, and (v) The inception of a comprehensible and foolproof web-server regarding the predictor and to ensure its receptiveness and accessibility to the public.…”

Machine learning model to predict oncologic outcomes for drugs in randomized clinical trials

“…16,17 In 2015, very powerful webservers such as "Pse-in-One" 18 and its updated version "Pse-in-One2.0" 19 were established and used to generate any desired feature vectors for protein/peptide and DNA/RNA sequences. 19 We are inspired by the work done to extract relevant features, the use of webservers, and also by Chou's 5-step rule 20 as fully introduced in our method section (the rule has been widely employed in a variety of applications in driving proteome/genome analysis and drug development, as seen in recent papers [20][21][22] ). In our work, we will follow the 5-step rule to formulate an algorithm that requires first obtaining important features (eg, genomic, proteomic, transcriptomic and clinical outcome information) and then applying ML to extract pattern information between our chosen attributes and clinical outcome.…”