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A novel approach to high throughput microbial genome finishing: Incorporation of 454 sequence data for gap closure in low quality Sanger data., , , , and . BIOCOMP, page 169-174. CSREA Press, (2007)Predicting tumor cell line response to drug pairs with deep learning., , , , , , , , , and 2 other author(s). BMC Bioinform., 19-S (18): 71-79 (2018)A cross-study analysis of drug response prediction in cancer cell lines., , , , , , , , , and 14 other author(s). Briefings Bioinform., (2022)SVDMAN-singular value decomposition analysis of microarray data., , and . Bioinform., 17 (6): 566-568 (2001)Learning Curves for Drug Response Prediction in Cancer Cell Lines., , , , , , , , , and 2 other author(s). CoRR, (2020)Deep learning methods for drug response prediction in cancer: predominant and emerging trends., , , , , , and . CoRR, (2022)Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discovery., , , , , , , , , and 6 other author(s). ICLR, OpenReview.net, (2022)High-bypass Learning: Automated Detection of Tumor Cells That Significantly Impact Drug Response., , , , , , , and . MLHPC/AI4S@SC, page 40-49. IEEE, (2020)Learning curves for drug response prediction in cancer cell lines., , , , , , , , , and 2 other author(s). BMC Bioinform., 22 (1): 252 (2021)GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics., , , , , , , , , and 26 other author(s). Int. J. High Perform. Comput. Appl., 37 (6): 683-705 (November 2023)