{"id":"W4297475121","doi":"10.1002/pro.4442","title":"CSM‐peptides: A computational approach to rapid identification of therapeutic peptides","year":2022,"lang":"en","type":"article","venue":"Protein Science","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Health and Medical Research Council; Medical Research Council Canada; Medical Research Council; State Government of Victoria; University of Queensland","keywords":"Identification (biology); Computational biology; Peptide; Chemistry; Combinatorial chemistry; Biochemistry; 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":[0.002267056,0.001312181,0.001499823,0.001714624,0.0005598342,0.001302675,0.001532547,0.001571816,0.003425797],"category_scores_gemma":[0.005839108,0.000614193,0.001272374,0.001192475,0.0005921363,0.001037621,0.0010405,0.001850199,0.0008394979],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006772313,"about_ca_system_score_gemma":0.002291418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003044529,"about_ca_topic_score_gemma":0.004036316,"domain_scores_codex":[0.9992329,0.000311905,0.00005908638,0.0001512578,0.0001932994,0.00005149494],"domain_scores_gemma":[0.9971306,0.002227446,0.0001528657,0.0001260862,0.0002554334,0.0001076004],"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":[0.001455779,0.0004836724,0.005230634,0.0007270685,0.0005604687,0.0002736313,0.00008425355,0.7471688,0.009634352,0.008992112,0.01186549,0.2135237],"study_design_scores_gemma":[0.00003976657,0.0000511221,0.0001004588,0.000006290636,0.00001339109,0.00002477062,0.000005208574,0.9957933,0.001111721,0.001986753,0.0008598715,0.000007208894],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06182453,0.001447015,0.915789,0.001233488,0.0002683039,0.0004906197,0.002342918,0.01384661,0.002757479],"genre_scores_gemma":[0.226042,0.0005688048,0.7668153,0.0006618482,0.0001350982,0.001107034,0.002604181,0.0005066312,0.001559005],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003425797,"threshold_uncertainty_score":0.01198953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01237875106079134,"score_gpt":0.2690083737431696,"score_spread":0.2566296226823783,"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."}}