{"id":"W4308787105","doi":"10.5772/intechopen.108422","title":"Perspective Chapter: Pattern Recognition for Mass-Spectrometry-Based Proteomics","year":2022,"lang":"en","type":"book-chapter","venue":"Biomedical engineering","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Jewish General Hospital","funders":"","keywords":"Proteomics; Proteogenomics; Computer science; Artificial intelligence; Biomarker discovery; Computational biology; Metabolomics; Genomics; Pattern recognition (psychology); Bioinformatics; Chemistry; Biology; Genome; Gene","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001043508,0.0003832627,0.0003512314,0.0002340485,0.0001124835,0.00002397359,0.000294697,0.0004099889,0.003800951],"category_scores_gemma":[0.00006016432,0.0004440719,0.0002575875,0.00005106914,0.00006524195,0.00004012048,0.00007710664,0.0007436377,0.00001896449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005645028,"about_ca_system_score_gemma":0.00006271262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008467628,"about_ca_topic_score_gemma":3.688725e-7,"domain_scores_codex":[0.9984238,0.000001321945,0.0003645551,0.0005734037,0.0003155713,0.0003214217],"domain_scores_gemma":[0.9990726,0.00009549965,0.0001948453,0.0003792325,0.00008977294,0.0001680377],"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":[0.0001589917,0.0002138368,0.000002453835,0.002013354,0.0005144884,0.00005962519,0.0001145964,0.0003782385,0.6419659,0.3123067,0.0007254519,0.04154631],"study_design_scores_gemma":[0.001445413,0.0002644087,5.472434e-7,0.0006512151,0.0001902093,0.00002501505,0.00003358733,0.02496009,0.07166933,0.05742869,0.8413787,0.001952791],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0000260319,0.0001659899,0.9626324,0.0004933936,0.00009737872,0.000754683,0.00183982,0.0005799277,0.03341035],"genre_scores_gemma":[0.003530466,0.0003554557,0.965554,0.0001789646,0.001537118,0.004185694,0.003580506,0.0005483464,0.02052947],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8406532,"threshold_uncertainty_score":0.9998011,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01656566182805001,"score_gpt":0.2427667255331042,"score_spread":0.2262010637050542,"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."}}