{"id":"W4379518401","doi":"10.21428/594757db.033df5af","title":"An Explainable Deep Few-shot Network for Protein Family Classification","year":2023,"lang":"en","type":"article","venue":"","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"National Research Council Canada; Concordia University","keywords":"Shot (pellet); Artificial intelligence; Computer science; Pattern recognition (psychology); Chemistry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003673603,0.0001104773,0.00008537992,0.00003096076,0.0001494399,0.0000431012,0.0001950711,0.0001329268,0.00001824719],"category_scores_gemma":[0.00009232938,0.0001014944,0.00004924587,0.0001474337,0.00002533472,0.000006232904,0.00004668011,0.0000615603,0.00005710003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009376803,"about_ca_system_score_gemma":0.00003435176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008876165,"about_ca_topic_score_gemma":0.00002036467,"domain_scores_codex":[0.999181,0.00003464549,0.0001890535,0.0002046309,0.00009112311,0.0002995304],"domain_scores_gemma":[0.9994045,0.000008553728,0.00007175982,0.0003744456,0.00007551847,0.00006515683],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001526484,0.00006474716,0.00298527,0.0001409263,0.00005056397,7.191248e-7,0.0001443531,0.0131298,0.8988901,0.00996702,0.06224059,0.0122332],"study_design_scores_gemma":[0.0009660717,0.001105882,0.01358909,0.00001947415,0.00001944275,0.000004328916,0.0009996134,0.4621916,0.04238185,0.001250262,0.4768992,0.0005731388],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7536969,0.00007544367,0.222097,0.000462667,0.0002208285,0.001361186,0.00001432112,0.00026271,0.02180895],"genre_scores_gemma":[0.9482018,0.00002218069,0.0422574,0.0005006981,0.000474743,0.0003644875,0.001525568,0.00003746459,0.006615611],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8565083,"threshold_uncertainty_score":0.413882,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02727523190154491,"score_gpt":0.3005732698457717,"score_spread":0.2732980379442268,"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."}}