{"id":"W4386314734","doi":"10.56588/iabcd.v2i2.97","title":"COMPUTATIONAL ANALYSIS OF TRANSCRIPTION FACTORS AS CANCER DRUG TARGETS WITH POTENTIAL INHIBITORS FROM THE NPACT DATABASE","year":2023,"lang":"en","type":"article","venue":"International Association of Biologicals and Computational Digest","topic":"Phytochemicals and Medicinal Plants","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Impact","funders":"","keywords":"Transcription factor; Docking (animal); Transcription (linguistics); Computational biology; Biology; Drug discovery; Chemistry; Cancer cell; Cancer research; Bioinformatics; Cell biology; Genetics; Cancer; Gene; Medicine","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":[0.0004080746,0.001271746,0.001477317,0.001408033,0.0004034473,0.0009898966,0.001190146,0.0009109444,0.006520791],"category_scores_gemma":[0.001679368,0.0002938358,0.001679029,0.0013908,0.0002053732,0.0003795229,0.0003640738,0.0005226777,0.0007107395],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006971087,"about_ca_system_score_gemma":0.001239992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007162124,"about_ca_topic_score_gemma":0.01216905,"domain_scores_codex":[0.9998121,0.00005708939,0.00001769944,0.00004620489,0.00004363009,0.00002333574],"domain_scores_gemma":[0.9995616,0.0003219784,0.00003059289,0.00002108355,0.00003748824,0.00002711088],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003167865,0.0008781718,0.02566075,0.005598967,0.00185489,0.001564571,0.0001059465,0.8311757,0.006987515,0.006374558,0.04854004,0.06809099],"study_design_scores_gemma":[0.0004559571,0.0004805898,0.004206832,0.00008499279,0.000456771,0.0003374834,0.0001055942,0.9710853,0.002385535,0.003106523,0.01726659,0.00002791044],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7611087,0.01274246,0.03829588,0.002871196,0.0003091052,0.0006221013,0.1540961,0.00875019,0.02120436],"genre_scores_gemma":[0.7689621,0.003799737,0.05551808,0.0005416009,0.0000781265,0.0009350228,0.1667519,0.000361111,0.003052402],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007162124,"threshold_uncertainty_score":0.02181417,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01462931535981009,"score_gpt":0.2849571503214069,"score_spread":0.2703278349615968,"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."}}