{"id":"W4413800236","doi":"10.1101/2025.08.25.671769","title":"APDeeM: A machine Learning strategy towards Effective Peptide Vaccine Candidates Identification against Different Types of Viruses","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"vaccines and immunoinformatics approaches","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Biotechnology Research Institute","funders":"Green University","keywords":"Expediting; Boosting (machine learning); AdaBoost; Machine learning; Identification (biology); Computer science; Artificial intelligence; Random forest; Support vector machine; Engineering; Biology","routes":{"ca_aff":true,"ca_fund":false,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000368323,0.0005183219,0.0005992996,0.0002025583,0.0001241711,0.0001229825,0.0005047676,0.0004387683,0.00001052796],"category_scores_gemma":[0.0002555234,0.0004803646,0.0002108178,0.0002112943,0.00004359384,0.00001467782,0.0006612062,0.0004789376,0.00000636682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007687436,"about_ca_system_score_gemma":0.0002900505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001521309,"about_ca_topic_score_gemma":0.00001972235,"domain_scores_codex":[0.9979945,0.0001424624,0.0007090541,0.0006105439,0.0002079411,0.0003354958],"domain_scores_gemma":[0.9979237,0.00002693341,0.0006486261,0.0008836336,0.0004210119,0.0000961129],"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":[0.0001365348,0.0001463006,0.01465721,0.0009972031,0.0005532505,0.000001609674,0.00001731444,0.001466152,0.9817338,0.0000797207,0.0001536923,0.00005722078],"study_design_scores_gemma":[0.0006118643,0.0001653325,0.09617103,0.0002905656,0.0001767244,1.526253e-8,0.00000985348,0.0034558,0.8971232,0.000001840599,0.0015065,0.0004872353],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9925033,0.004348339,0.001498894,0.00003222735,0.000329103,0.0007875793,0.000389335,0.00006696855,0.0000442061],"genre_scores_gemma":[0.9972616,0.001824265,0.0003757415,0.00005619928,0.0001556898,0.0001986621,0.00002272383,0.00005576186,0.00004936112],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08461055,"threshold_uncertainty_score":0.9997648,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01007854971766315,"score_gpt":0.2286832688350394,"score_spread":0.2186047191173763,"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."}}