{"id":"W4225886114","doi":"10.1186/s13073-021-01000-y","title":"Predicting heterogeneity in clone-specific therapeutic vulnerabilities using single-cell transcriptomic signatures","year":2021,"lang":"en","type":"article","venue":"Genome Medicine","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Genome Institute of Singapore","keywords":"Computational biology; In silico; Transcriptome; clone (Java method); Precision medicine; Repurposing; Drug repositioning; Precision oncology; Genetic heterogeneity; Tumor heterogeneity; Computer science; Bioinformatics; Biology; Gene; Cancer; Drug; Gene expression; Genetics; Phenotype; Pharmacology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001856308,0.0006824541,0.0008890252,0.0009261364,0.000339979,0.001265893,0.0004889332,0.0007637281,0.003336373],"category_scores_gemma":[0.004909113,0.0003148024,0.0009456735,0.0007666539,0.0003366534,0.0005455313,0.0006942992,0.0007297592,0.001598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006856821,"about_ca_system_score_gemma":0.0009868836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002282009,"about_ca_topic_score_gemma":0.006555502,"domain_scores_codex":[0.9993207,0.00009116324,0.00005475344,0.0002886989,0.0001910305,0.00005350166],"domain_scores_gemma":[0.9976267,0.001449646,0.0002437283,0.0003184793,0.0002627022,0.00009878373],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001354334,0.0001790913,0.2079046,0.001798928,0.0009121692,0.0009118317,0.0003541324,0.1002698,0.5163022,0.003718395,0.01902625,0.1472682],"study_design_scores_gemma":[0.0001881356,0.0007423837,0.1330228,0.0002232378,0.0008823402,0.001409305,0.0003070182,0.5422497,0.2687406,0.01488799,0.0371536,0.0001929331],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6438664,0.004273059,0.2291043,0.001240981,0.0002453498,0.0004351549,0.1034439,0.00912685,0.008263859],"genre_scores_gemma":[0.8107495,0.001349324,0.1175457,0.0009157921,0.00008066718,0.0003488518,0.06580819,0.0007666192,0.002435226],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003336373,"threshold_uncertainty_score":0.01116127,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03103038251369962,"score_gpt":0.257925320735246,"score_spread":0.2268949382215464,"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."}}