{"id":"W4412593129","doi":"10.1007/978-3-031-94039-2_13","title":"GDAdaP: A Domain Adaptation Framework for Gene Dependency Prediction in Lung Cancer","year":2025,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Dependency (UML); Adaptation (eye); Domain adaptation; Lung cancer; Domain (mathematical analysis); Computer science; Computational biology; Biology; Internal medicine; Artificial intelligence; Medicine; Mathematics; Neuroscience","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":[0.0007143302,0.001058054,0.0008179932,0.001288171,0.0004837764,0.000728363,0.001604826,0.0007900975,0.006244099],"category_scores_gemma":[0.001594036,0.0004480187,0.001429994,0.001462452,0.0002522454,0.0007650211,0.00117973,0.001569692,0.003285248],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004577619,"about_ca_system_score_gemma":0.0007683574,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006475538,"about_ca_topic_score_gemma":0.01320974,"domain_scores_codex":[0.9997059,0.00005225463,0.00001841947,0.000130446,0.00006459696,0.00002830703],"domain_scores_gemma":[0.9996071,0.000225419,0.00001682531,0.00006556589,0.00005832341,0.00002677354],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005726896,0.0002060752,0.006271561,0.0005371997,0.0003282366,0.0005291555,0.00018169,0.06493081,0.0189532,0.004959806,0.13937,0.7631596],"study_design_scores_gemma":[0.0001079716,0.00009355624,0.004099892,0.00006241611,0.0001266557,0.0004713788,0.000105605,0.9099525,0.01398581,0.02297118,0.04796677,0.00005620314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0201284,0.001245089,0.8461139,0.0003518478,0.0001833407,0.0002053361,0.01349103,0.1155221,0.00275895],"genre_scores_gemma":[0.1286482,0.0009048455,0.8217103,0.0005700917,0.0001217937,0.0005022484,0.03699448,0.004825053,0.005722997],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006475538,"threshold_uncertainty_score":0.02088857,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01902068210711665,"score_gpt":0.2939066544018691,"score_spread":0.2748859722947524,"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."}}