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• W3154491202 abstract "Introduction: The need for designing and developing personalized drugs for various diseases has become a challenging research topic at present. The individual variation towards susceptibility of a drug depends upon the genomic, epigenomic, metagenomic and environmental genomic factors. Areas covered: The ‘Single Nucleotide Variant (SNV)’ has been identified as the functional feature corresponding these factors. The need for personalized drug designing for the ERBB2 mutation related to Breast Cancer has been proposed by taking the South Asian (SA) population as the test sample. The SNVs corresponding to SA population for the ERBB2 mutation has been identified. The ‘convolution neural network-based deep learning technique’ (DeepCNN) has been used for computing the clinical significance of the SNVs, whose clinical significance values are unknown, using the functional variants as the attributes for the ethnic group. Expert opinion: The population has been classified into four groups based upon the probability of variants. The population-specific gene models and protein models have been designed. The potential molecules that control ERBB2 mutation specific to the South Asian population have been identified through docking/interaction score values" @default.
• W3154491202 created "2021-04-26" @default.
• W3154491202 creator A5062175092 @default.
• W3154491202 creator A5082558889 @default.
• W3154491202 date "2021-04-15" @default.
• W3154491202 modified "2023-09-27" @default.
• W3154491202 title "Identification of Lapatinib Derivatives and Analogs to Control Metastatic Breast Cancer-specific to South Asian Population-a Pharmacogenomic Approach" @default.
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• W3154491202 doi "https://doi.org/10.37394/23208.2021.18.6" @default.
• W3154491202 hasPublicationYear "2021" @default.
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