{"id":"W2807152622","doi":"10.1101/331926","title":"Deep Neural Network for Protein Contact Prediction by Weighting Sequences in a Multiple Sequence Alignment","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute of Genetics; Japan Agency for Medical Research and Development","keywords":"Weighting; Artificial intelligence; Artificial neural network; Computer science; Sequence (biology); Pattern recognition (psychology); Correlation; Supervised learning; Machine learning; Deep learning; Mathematics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006908917,0.0006514741,0.0007125522,0.0008973309,0.0002958276,0.0004136405,0.0009243424,0.0009828405,0.001752909],"category_scores_gemma":[0.001215921,0.0003502744,0.0004426275,0.0008636206,0.0002757517,0.0008558166,0.0005366335,0.000871391,0.0004519308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001007125,"about_ca_system_score_gemma":0.0006620444,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006573317,"about_ca_topic_score_gemma":0.009088583,"domain_scores_codex":[0.9998071,0.00004172866,0.00001104462,0.0000545156,0.00004962802,0.00003602584],"domain_scores_gemma":[0.9996215,0.0001454718,0.00005673714,0.00003380816,0.0001087333,0.00003377445],"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":[0.000360321,0.0003244586,0.004638668,0.00008979823,0.0001300922,0.0001198921,0.00003906236,0.7022942,0.01533167,0.003484553,0.003340573,0.2698468],"study_design_scores_gemma":[0.000001746985,0.00000596986,0.0001231208,0.000001121271,0.000001886379,0.000003062173,0.000001051229,0.9989236,0.0005302796,0.0003642161,0.00004290109,9.73131e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3398762,0.001241038,0.6516865,0.0006390131,0.00009024933,0.00008494366,0.0004628534,0.003248377,0.00267084],"genre_scores_gemma":[0.8666239,0.0002401595,0.1289336,0.0001327814,0.00003426339,0.00007417038,0.000669273,0.0000591467,0.003232565],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006573317,"threshold_uncertainty_score":0.01307011,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01143445456606948,"score_gpt":0.2217247098355099,"score_spread":0.2102902552694404,"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."}}