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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11672063 | PMC |
| http://dx.doi.org/10.1136/archdischild-2024-327165 | DOI Listing |
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With the advancements of next-generation sequencing, publicly available pharmacogenomic datasets from cancer cell lines provide a handle for developing predictive models of drug responses and identifying associated biomarkers. However, many currently available predictive models are often just used as black boxes, lacking meaningful biological interpretations. In this study, we made use of open-source drug response data from cancer cell lines, in conjunction with KEGG pathway information, to develop sparse neural networks, K-net, enabling the prediction of drug response in EGFR signaling pathways and the identification of key biomarkers.
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