{"id":"W3216980854","doi":"10.1186/s12859-021-04484-y","title":"A multitask transfer learning framework for the prediction of virus-human protein–protein interactions","year":2021,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute for Catastrophic Loss Reduction; Gottfried Wilhelm Leibniz Universität Hannover; Niedersächsische Ministerium für Wissenschaft und Kultur","keywords":"Transfer of learning; Computer science; Computational biology; Protein–protein interaction; Multi-task learning; Artificial intelligence; Machine learning; Biology; Task (project management); Genetics; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0002300481,0.0001543897,0.0001575176,0.00003249609,0.0002666294,0.00005216207,0.0001779126,0.0001896846,0.00003474937],"category_scores_gemma":[0.0001617037,0.000121585,0.0001946329,0.0001109096,0.00008469256,0.00001633818,0.00007550803,0.0002436203,0.000006758095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001419841,"about_ca_system_score_gemma":0.0001049113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006715488,"about_ca_topic_score_gemma":0.00005460943,"domain_scores_codex":[0.9989537,0.00002996783,0.0005473943,0.0001188809,0.0001280005,0.0002220418],"domain_scores_gemma":[0.9991887,0.00005380235,0.0001478265,0.0003750132,0.000181705,0.00005298033],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004916295,0.0004845797,0.0008354145,0.002344192,0.0009631323,9.933802e-7,0.004529113,0.02517088,0.8744559,0.04689306,0.003535473,0.04029559],"study_design_scores_gemma":[0.0023776,0.0009498028,0.0005965878,0.0005376025,0.0002286435,0.00003996662,0.004794555,0.3286618,0.497104,0.004594944,0.1594563,0.0006582447],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06701255,0.0002079103,0.9310109,0.0000776833,0.0001666381,0.0007500079,0.0001452956,0.00001932061,0.0006096641],"genre_scores_gemma":[0.7895759,0.00007605927,0.2074424,0.0001812693,0.0003973502,0.0002742987,0.0006032664,0.00003737359,0.001412076],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7235686,"threshold_uncertainty_score":0.4958093,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02031915709688368,"score_gpt":0.2618347627492409,"score_spread":0.2415156056523573,"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."}}