{"id":"W4386158194","doi":"10.1021/acs.analchem.3c00613","title":"Early Diagnosis: End-to-End CNN–LSTM Models for Mass Spectrometry Data Classification","year":2023,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"AI in cancer detection","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Fonds de Recherche du Québec - Santé; Société d'Accélération du Transfert de Technologies; Ministère de l'Enseignement supérieur, de la Recherche et de l'Innovation; Institut National Du Cancer; Institut National de la Santé et de la Recherche Médicale; Agence Nationale de la Recherche; Université de Lille","keywords":"Artificial intelligence; Computer science; Preprocessor; Convolutional neural network; Benchmark (surveying); Pattern recognition (psychology); Deep learning; Curse of dimensionality; Data pre-processing; Feature selection; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006466977,0.001283964,0.0006050118,0.0004688547,0.0002306338,0.0007563647,0.001607563,0.001288762,0.001956842],"category_scores_gemma":[0.001586229,0.0004065452,0.0005970696,0.000472385,0.000349586,0.001254905,0.0008418747,0.001827794,0.000782127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001195007,"about_ca_system_score_gemma":0.0009447802,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01077106,"about_ca_topic_score_gemma":0.01309798,"domain_scores_codex":[0.9998018,0.00003250183,0.00001154229,0.00006981465,0.0000366179,0.00004778617],"domain_scores_gemma":[0.9996307,0.0001537954,0.00003801094,0.00003207571,0.0001159159,0.00002943719],"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":[0.0004177391,0.000292054,0.003016842,0.0001540337,0.0001375281,0.0002351009,0.00008099987,0.5618854,0.01549313,0.005688785,0.007282763,0.4053157],"study_design_scores_gemma":[0.000002774231,0.00001851413,0.00009069229,0.000004370501,0.000006574891,0.00001035324,0.000002984052,0.9968336,0.001635378,0.001115941,0.0002753787,0.000003470207],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05797086,0.001644559,0.9308569,0.001130156,0.0002120218,0.0001024872,0.0005217753,0.004545711,0.003015532],"genre_scores_gemma":[0.8181702,0.0007720168,0.1679365,0.0008088759,0.0001115254,0.0001948553,0.001262028,0.0001464647,0.01059757],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01077106,"threshold_uncertainty_score":0.02141672,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0966839250659563,"score_gpt":0.3227062023904667,"score_spread":0.2260222773245104,"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."}}