{"id":"W4318751336","doi":"10.48550/arxiv.2301.12068","title":"Pre-Training Protein Encoder via Siamese Sequence-Structure Diffusion Trajectory Prediction","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Samsung; Tencent; Canadian Institute for Advanced Research; Natural Sciences and Engineering Research Council of Canada; Institut de Valorisation des Données; Microsoft Research","keywords":"Computer science; Encoder; Trajectory; Sequence (biology); Joint probability distribution; Conformational isomerism; Diffusion; Mutual information; Joint (building); Artificial intelligence; Algorithm; Mathematics; Engineering; Physics; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002021539,0.000395473,0.0002769579,0.0001659388,0.0001715653,0.00004423681,0.0005885121,0.0008302907,0.00005689499],"category_scores_gemma":[0.0001233313,0.0004326966,0.0002012731,0.0002066513,0.0001536422,0.00001326617,0.0008781172,0.000852884,0.0000235108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001064075,"about_ca_system_score_gemma":0.0002248521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009359576,"about_ca_topic_score_gemma":0.0001027342,"domain_scores_codex":[0.998254,0.0001488209,0.0002821774,0.0008114165,0.0001279502,0.0003756356],"domain_scores_gemma":[0.9985629,0.0000162464,0.0003291389,0.0008368989,0.0001112788,0.0001435766],"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":[0.0003114662,0.0000887597,0.01726948,0.0007845296,0.0003866658,0.0001148645,0.001675124,0.6781172,0.2982613,0.0004856436,0.0008724332,0.001632551],"study_design_scores_gemma":[0.001847897,0.0006735962,0.01645759,0.0006545,0.0003104228,0.00005190509,0.0007431812,0.9489755,0.01523922,0.007255333,0.005890854,0.001899975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9290862,0.00002847616,0.06872607,0.00003138442,0.0004790629,0.0006206954,0.000199812,0.0001974224,0.0006308521],"genre_scores_gemma":[0.9918868,0.00008375637,0.001242408,0.00005243049,0.0003176422,0.000004422503,0.001098799,0.0000620168,0.005251727],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.283022,"threshold_uncertainty_score":0.9998125,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05094002654711007,"score_gpt":0.2090181692533215,"score_spread":0.1580781427062114,"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."}}