{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002413822,0.0001227676,0.0003342259,0.0001624257,0.00006369365,0.00001500416,0.00009503508,0.00006957448,0.0001665142],"category_scores_gemma":[0.0001846733,0.00007351484,0.0001353167,0.0004531535,0.000092829,0.00008049596,0.00002930606,0.0001148646,0.000003509854],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007112281,"about_ca_system_score_gemma":0.00007997095,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005199592,"about_ca_topic_score_gemma":0.0000330719,"domain_scores_codex":[0.9985145,0.00005316999,0.0003923099,0.0002262483,0.0006932188,0.0001204915],"domain_scores_gemma":[0.9982192,0.0008351969,0.0003825034,0.00005688554,0.0004370108,0.00006920203],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004496433,0.0002606695,0.9292651,0.00001728336,0.002658422,0.000008218552,0.0005236078,0.04582635,0.01766793,0.0008978742,0.001767247,0.0006576277],"study_design_scores_gemma":[0.0007946763,0.00009001375,0.9802914,0.00008518292,0.000473793,0.000001686568,0.000241121,0.01486187,0.001141954,0.001625509,0.0003080764,0.0000847226],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9918932,0.0001302725,0.0004792249,0.004843537,0.0001385864,0.0001608677,0.002174599,0.00002632602,0.0001533751],"genre_scores_gemma":[0.9915248,0.0001834887,0.0002065706,0.0003514192,0.0001092169,0.00001164905,0.007499589,0.00000546463,0.0001078087],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05102627,"threshold_uncertainty_score":0.2997848,"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."}}