{"id":"W4284964920","doi":"10.1101/2022.07.06.499058","title":"The DynaSig-ML Python package: automated learning of biomolecular dynamics-function relationships","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Institute for Research in Immunology and Cancer","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Genome Canada; Canadian Institutes of Health Research; Fonds de Recherche du Québec - Santé; Compute Canada","keywords":"Python (programming language); Computer science; Pipeline (software); Software; In silico; Computational science; Artificial intelligence; Programming language; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001977458,0.001825836,0.001633312,0.001310537,0.000725523,0.001880988,0.003316807,0.001058721,0.06590675],"category_scores_gemma":[0.005206935,0.001266986,0.001975349,0.000981884,0.0009531988,0.001833933,0.00344912,0.003461058,0.03310741],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008692755,"about_ca_system_score_gemma":0.002737493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00319451,"about_ca_topic_score_gemma":0.004720539,"domain_scores_codex":[0.9990566,0.0002010983,0.00006089058,0.0002146773,0.000355704,0.0001110108],"domain_scores_gemma":[0.9986664,0.0006406253,0.0001029527,0.000291096,0.0001857869,0.0001130338],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004440554,0.0002097813,0.00325435,0.001842035,0.0004382108,0.000498257,0.0002449927,0.1177313,0.01038368,0.04451516,0.654654,0.1657841],"study_design_scores_gemma":[0.0003053667,0.00004537205,0.001460064,0.000128326,0.0000373132,0.0002203011,0.00003463558,0.8020878,0.01077111,0.05787091,0.1269106,0.0001281862],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.004406689,0.0002813899,0.5540809,0.0006157722,0.0001877535,0.0001792731,0.02970922,0.4041888,0.00635016],"genre_scores_gemma":[0.09139158,0.000577581,0.6948634,0.001304078,0.0001675492,0.00187367,0.05489102,0.140984,0.01394715],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.06590675,"threshold_uncertainty_score":0.22048,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007418613595846009,"score_gpt":0.2167833218580758,"score_spread":0.2093647082622297,"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."}}