{"id":"W4399504416","doi":"10.1158/1538-8514.synthleth24-b014","title":"Abstract B014: Unveiling the future: Exploring cutting-edge technologies for synthetic lethality discovery","year":2024,"lang":"en","type":"article","venue":"Molecular Cancer Therapeutics","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Synthetic lethality; Personalized medicine; Precision medicine; Drug discovery; CRISPR; Identification (biology); Cancer; Computer science; Computational biology; Cancer treatment; Data science; Medicine; Bioinformatics; Biology; DNA repair","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.005412793,0.0009009208,0.0007392759,0.001880857,0.001653205,0.006994423,0.001317564,0.002938204,0.01506276],"category_scores_gemma":[0.006901024,0.0005431403,0.0008763658,0.001214039,0.005316192,0.008442449,0.004303517,0.005458325,0.005303175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002889015,"about_ca_system_score_gemma":0.002514424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009554834,"about_ca_topic_score_gemma":0.0008647577,"domain_scores_codex":[0.9977748,0.0007048971,0.00009003729,0.0002528585,0.0009415849,0.0002358414],"domain_scores_gemma":[0.9961753,0.001877102,0.0003316906,0.0004311723,0.0007573382,0.0004274372],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003013486,0.0001311771,0.000699781,0.001356674,0.00004469938,0.0004900345,0.0005470959,0.002520744,0.02740151,0.5820934,0.1056745,0.2787392],"study_design_scores_gemma":[0.00006354662,0.0002942839,0.0004020827,0.0005541999,0.00004394146,0.0005888814,0.0004591121,0.003018535,0.03339468,0.3089257,0.6521374,0.0001175257],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02065441,0.1727172,0.320206,0.2289743,0.01677201,0.0004541787,0.001631493,0.003615031,0.2349754],"genre_scores_gemma":[0.3256095,0.209675,0.2954435,0.04912216,0.006193437,0.0007854414,0.001660686,0.001442379,0.1100679],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01506276,"threshold_uncertainty_score":0.05038995,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06713313725205455,"score_gpt":0.3410181780553131,"score_spread":0.2738850408032586,"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."}}