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Accurate prediction of anti drug re-
sponse (ADR) is challenging due to the uncer-
tainty of drug efficacy and heterogeneity of ge-
nome sickness patients. Strong evidences have
implicated the high dependence of ADR on pro-
files of individual patients. Precise identification
of ADR is crucial in both guiding drug design
and understanding genome sickness biology. In
this study, we present DeepADR which inte-
grates multi-omics profiles of genome sickness
cells and explores intrinsic chemical structures of
drugs for predicting ADR. Specifically,
DeepADR is a hybrid graph convolutional net-
work consisting of multiple subnetworks. Unlike
prior studies modeling hand-crafted features of
drugs, DeepADR automatically learns the latent
representation of topological structures among
atoms and bonds of drugs. The contribution of
different types of omics profiles for assessing
drug response is necessary.
Keywords:
Anti-genome sickness, Drug Re- sponse, Deep Learning, Genomics, Cell Line.
Cite Article:
" A Review Paper on Prediction of Antidrug Response Using Genetic Sequencing via Deep Learning", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 11, page no.a112-a115, November-2025, Available :http://www.ijrti.org/papers/IJRTI2511015.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator