Mehdi Hassan1,2, Safdar Ali3, Jin Young Kim2,*, Muhammad Sanaullah4, Hani Alquhayz5, Khushbakht Safdar6
CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1643-1663, 2023, DOI:10.32604/cmc.2023.038578
- 30 August 2023
Abstract 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 More >
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