{"entity":{"id":"drug-response-baselines","kind":"term","name":"Drug-response baselines and frameworks: mean-drug floor, LightGBM, DrEval, IMPROVE, DeepTTA","aka":["naive mean-drug baseline","mean-cell-line baseline","DrEval","IMPROVE benchmark","GraphDRP","UNO model","DeepTTA","DeepCCDS","LightGBM","gradient-boosted trees"],"tldr":"The floor for any drug-response model is predicting each drug's average effect across cell lines; tuned gradient-boosted trees such as LightGBM often tie or beat deep models under honest splits, which is why evaluation frameworks now exist.","summary":"LightGBM is Microsoft's free gradient-boosting framework based on decision trees (Wikipedia) and a common strong baseline. DrEval (Bernett and colleagues) and the IMPROVE project from Argonne and the NCI (with models such as UNO and GraphDRP) benchmark deep drug-response models against naive baselines under fixed protocols; the naive mean-drug predictor is hard to beat because most variance in a screen is between drugs, not between cell lines. Transformer models such as DeepTTA (Jiang and colleagues) report headline correlations that fall under leave-cell-line-out and cross-study splits.","asOf":"2026-09-24","wikipedia":"https://en.wikipedia.org/wiki/LightGBM","links":[{"label":"Bernett et al., DrEval (Nature Communications 2026)","url":"https://doi.org/10.1038/s41467-026-72903-w"},{"label":"IMPROVE project (Argonne and NCI) on GitHub","url":"https://github.com/JDACS4C-IMPROVE"},{"label":"Jiang et al., DeepTTA: a transformer-based model for predicting cancer drug response (Briefings in Bioinformatics 2022)","url":"https://doi.org/10.1093/bib/bbac100"},{"label":"Wikipedia","url":"https://en.wikipedia.org/wiki/LightGBM"}],"tags":["cansim-terms"],"related":["cancer-ai-vocabulary"],"cancers":[],"sections":[],"technologies":[],"targets":[],"drugs":[],"companies":[],"institutions":[],"pathways":[],"terms":["drug-response-splits","ridge-regression","spearman-correlation"],"trials":[],"people":[],"bottlenecks":[],"keyPapers":[],"journals":[],"dependsOn":[],"notes":["Listed in the CanSim terms map 1.0.0 (docs/onco/terms.json, generated 2026-09-24), CC BY 4.0, attribution: CanSim project, an open, public-data-first cancer foundation-model programme; CanSim page path /terms/drug-response-frontier."],"provenance":{"editedBy":"OnCo CanSim terms wave (Wikipedia summaries, standards and project pages, GDC and FDA pages, Europe PMC)","editedOn":"2026-09-24","note":"CanSim terms map 1.0.0 (docs/onco/terms.json, generated 2026-09-24), CC BY 4.0, attribution: CanSim project, an open, public-data-first cancer foundation-model programme"},"category":"Methods and models"},"route":"/terms/drug-response-baselines/","neighbours":{"term":[{"id":"leaderboard-benchmark","kind":"term","name":"Benchmarks, leaderboards and contamination","route":"/terms/leaderboard-benchmark/"},{"id":"cancer-ai-vocabulary","kind":"term","name":"Cancer AI vocabulary (CanSim terms map)","route":"/terms/cancer-ai-vocabulary/"},{"id":"drug-response-splits","kind":"term","name":"Drug-response data splits: leave-cell-line-out, leave-drug-out, leave-tissue-out","route":"/terms/drug-response-splits/"},{"id":"ridge-regression","kind":"term","name":"Ridge regression and the multilayer perceptron","route":"/terms/ridge-regression/"},{"id":"spearman-correlation","kind":"term","name":"Spearman rank correlation","route":"/terms/spearman-correlation/"}]}}