{"id":"W4366549780","doi":"10.1093/bioinformatics/btad180","title":"The DynaSig-ML Python package: automated learning of biomolecular dynamics–function relationships","year":2023,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Institute for Research in Immunology and Cancer","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Compute Canada; Genome Canada","keywords":"Python (programming language); Computer science; R package; Programming language; Artificial intelligence","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":[0.00188714,0.002185035,0.001908955,0.001279707,0.0008973248,0.001958323,0.003623307,0.001187655,0.05374631],"category_scores_gemma":[0.005682231,0.001525683,0.00224963,0.001005587,0.0009581434,0.002247058,0.003258953,0.004182345,0.03103147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00100195,"about_ca_system_score_gemma":0.003078449,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003552887,"about_ca_topic_score_gemma":0.006156976,"domain_scores_codex":[0.9991956,0.0001660442,0.00005458143,0.0002012692,0.0002814918,0.0001009839],"domain_scores_gemma":[0.9986528,0.0006821904,0.0001231203,0.0002605058,0.0001671475,0.0001142064],"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":[0.0005586158,0.0002801657,0.005684472,0.003391129,0.0007846105,0.0006060711,0.0004333665,0.06508208,0.01592829,0.03452239,0.7272296,0.1454993],"study_design_scores_gemma":[0.0003950617,0.00009736841,0.003274819,0.000220617,0.00008939978,0.0004659162,0.00007252711,0.7077289,0.01970889,0.06675479,0.2009257,0.0002661697],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"software","genre_gemma":"software","genre_scores_codex":[0.006048928,0.0004290821,0.4489043,0.000559537,0.0002234777,0.0002177285,0.03931046,0.4983672,0.005939272],"genre_scores_gemma":[0.08850536,0.001073932,0.6344059,0.001681348,0.0001875787,0.002490334,0.07492243,0.1829431,0.01378994],"genre_candidate":"software","genre_consensus":"software","teacher_disagreement_score":0.05374631,"threshold_uncertainty_score":0.1797993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00876049196145396,"score_gpt":0.2371094097420158,"score_spread":0.2283489177805619,"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."}}