AntiFlamPred: An Anti-Inflammatory Peptide Predictor for Drug Selection Strategies

作     者：Fahad Alotaibi Muhammad Attique Yaser Daanial Khan 

作者机构：Department of Information SystemFaculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer ScienceUniversity of Management and TechnologyLahore54000Pakistan Department of Information TechnologyUniversity of GujratGujrat50700Pakistan 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2021年第69卷第10期

页      面：1039-1055页

核心收录：

学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

基　　金：This project was funded by the Deanship of Scientific Research(DSR) King Abdulaziz University(https://www.kau.edu.sa/) Jeddah under Grant No.(D-49-611-1441) 

主　　题：Prediction feature extraction machine learning bootstrap aggregation deep learning bioinformatics computational intelligence antiinflammatory peptides 

摘      要：Several autoimmune ailments and inflammation-related diseases emphasize the need for peptide-based therapeutics for their treatment and established substantial ***,the wet-lab experiments for the investigation of anti-inflammatory proteins/peptides(“AIP)are usually very costly and remain ***,before wet-lab investigations,it is essential to develop in-silico identification models to classify prospective anti-inflammatory candidates for the facilitation of the drug development *** anti-inflammatory prediction tools have been proposed in the recent past,yet,there is a space to induce enhancement in prediction performance in terms of precision and *** exceedingly accurate antiinflammatory prediction model is proposed,named AntiFlamPred(“Antiinflammatory Peptide Predictor),by incorporation of encoded features and probing machine learning algorithms including deep *** proposed model performs best in conjunction with deep *** testing and validation were applied including cross-validation,self-consistency,jackknife,and independent set *** proposed model yielded 0.919 value for area under the curve(AUC)and revealed Mathew’s correlation coefficient(MCC)equivalent to 0.735 demonstrating its effectiveness and ***,the proposed model was also extensively probed in comparison with other existing *** performance of the proposed model also out-performs other existing *** outcomes establish that the proposed model is a robust predictor for identifying AIPs and may subsidize well in the extensive lab-based ***,it has the potential to assiduously support medical and bioinformatics research.