Developing a Breast Cancer Resistance Protein Substrate Prediction System Using Deep Features and LDA

作     者：Mehdi Hassan Safdar Ali Jin Young Kim Muhammad Sanaullah Hani Alquhayz Khushbakht Safdar 

作者机构：Department of Computer ScienceAir UniversitySector E-9PAF ComplexIslamabad44000Pakistan Department of ICT Convergence System EngineeringChonnam National UniversityGwangjuKorea Directorate of National RepositoryIslamabadPakistan Department of Computer ScienceBahauddin Zakariya UniversityMultan60000Pakistan Department of Computer Science and InformationCollege of Science in ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia Pakistan Atomic Energy CommissionGeneral HospitalIslamabadPakistan 

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

年 卷 期：2023年第76卷第8期

页      面：1643-1663页

核心收录：

学科分类：0710[理学-生物学] 1002[医学-临床医学] 1001[医学-基础医学(可授医学、理学学位)] 100214[医学-肿瘤学] 0703[理学-化学] 0714[理学-统计学（可授理学、经济学学位）] 10[医学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the BK21 FOUR Program(FosteringOutstanding Universities for Research 5199991714138)funded by the Ministry of Education(MOE Korea)and the National Research Foundation of Korea(NRF). 

主　　题：BCRP drug response deep learning transfer learning LDA In silico 

摘      要：Breast cancer resistance protein(BCRP)is an important resistance protein that significantly impacts anticancer drug discovery,treatment,and rehabilitation.Early identification of BCRP substrates is quite a challenging task.This study aims to predict early substrate structure,which can help to optimize anticancer drug development and clinical diagnosis.For this study,a novel intelligent approach-based methodology is developed by modifying the ResNet101 model using transfer learning(TL)for automatic deep feature(DF)extraction followed by classification with linear discriminant analysis algorithm(TLRNDF-LDA).This study utilized structural fingerprints,which are exploited by DF contrary to conventional molecular descriptors.The proposed in silico model achieved an outstanding accuracy performance of 98.56%on test data compared to other state-of-the-art approaches using standard quality measures.Furthermore,the model’s efficacy is validated via a statistical analysisANOVAtest.It is demonstrated that the developedmodel can be used effectively for early prediction of the substrate structure.The pipeline of this study is flexible and can be extended for in vitro assessment efficacy of anticancer drug response,identification of BCRP functions in transport experiments,and prediction of prostate or lung cancer cell lines.