{"id":"W4406562207","doi":"10.1016/j.slasd.2025.100214","title":"Single-plate kinome screening in live-cells to enable highly cost-efficient kinase inhibitor profiling","year":2025,"lang":"en","type":"article","venue":"SLAS DISCOVERY","topic":"Chronic Lymphocytic Leukemia Research","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Genentech; Deutschen Konsortium für Translationale Krebsforschung; Ontario Genomics Institute; European Federation of Pharmaceutical Industries and Associations; McGill University; Innovative Health Initiative; Ontario Genomics; Genome Canada; Bayer; Pfizer; Deutsche Forschungsgemeinschaft; Deutsches Krebsforschungszentrum; Bristol-Myers Squibb","keywords":"Kinome; Profiling (computer programming); Kinase; Computational biology; Computer science; Cell biology; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001243859,0.001112305,0.001044013,0.0007732121,0.0007868441,0.001133381,0.001145574,0.001020547,0.008034407],"category_scores_gemma":[0.0007621428,0.0005440527,0.0006394778,0.0005580545,0.0005956565,0.0008408625,0.001329495,0.001871123,0.007044877],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005927142,"about_ca_system_score_gemma":0.0007199654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007964377,"about_ca_topic_score_gemma":0.003366366,"domain_scores_codex":[0.9986323,0.0001770569,0.0001114952,0.0003271411,0.0005330644,0.0002188844],"domain_scores_gemma":[0.9994763,0.0001175379,0.00006482698,0.0001623717,0.000117866,0.00006123949],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001456087,0.0001008711,0.0001818599,0.0001771638,0.0000209073,0.0001119555,0.00006582661,0.000355054,0.9854634,0.001349007,0.002022976,0.0100054],"study_design_scores_gemma":[0.00002791136,0.000282329,0.001072043,0.00003068687,0.00002610811,0.0003010704,0.00005042646,0.002377241,0.9747972,0.0005294174,0.02046883,0.00003661071],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1883162,0.004020025,0.7430373,0.001333646,0.0005277328,0.004299596,0.009521208,0.008914937,0.04002923],"genre_scores_gemma":[0.4845587,0.005978837,0.4437146,0.001184011,0.00009335178,0.006361882,0.01355361,0.001029995,0.04352497],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008034407,"threshold_uncertainty_score":0.02687776,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02658874651330027,"score_gpt":0.2995148444599841,"score_spread":0.2729260979466838,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}