{"id":"W4384829609","doi":"10.1101/2023.07.17.549396","title":"Zero-Shot Transfer of Protein Sequence Likelihood Models to Thermostability Prediction","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada","keywords":"Thermostability; Complementarity (molecular biology); Computer science; Machine learning; Artificial intelligence; Stability (learning theory); Task (project management); Sequence (biology); Biology; Engineering","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":[0.002786144,0.0008754644,0.0008960463,0.0007826646,0.0003407304,0.000957928,0.001997209,0.001436346,0.001354813],"category_scores_gemma":[0.007538706,0.0004792095,0.0008698277,0.0005499974,0.001051975,0.001613835,0.001630993,0.002022695,0.0006020514],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001354336,"about_ca_system_score_gemma":0.001076063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00364593,"about_ca_topic_score_gemma":0.002763202,"domain_scores_codex":[0.9992664,0.0003282185,0.00002296636,0.000171151,0.0001563477,0.00005479978],"domain_scores_gemma":[0.9973049,0.001673999,0.0002410521,0.0003216093,0.0003283207,0.000129999],"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":[0.0003255627,0.0002331109,0.002527612,0.0001135624,0.0001402702,0.00008471252,0.00006123422,0.9228193,0.00819529,0.003308407,0.001830634,0.06036035],"study_design_scores_gemma":[0.000002643224,0.00001070091,0.00007960596,0.000001286945,0.000001551082,0.000003738224,0.000002156362,0.9975451,0.001064062,0.00124199,0.00004451191,0.000002603178],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2366424,0.0005053683,0.7523755,0.0005764447,0.00007805267,0.00008682975,0.0003764644,0.007561324,0.001797707],"genre_scores_gemma":[0.9045868,0.0001159014,0.09236221,0.0001920372,0.00003916638,0.0001075215,0.0007555197,0.000258368,0.001582465],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00364593,"threshold_uncertainty_score":0.01473475,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04027508816842713,"score_gpt":0.2353716093710906,"score_spread":0.1950965212026635,"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."}}