{"id":"W2750032252","doi":"10.3390/pr5030047","title":"Characterizing Gene and Protein Crosstalks in Subjects at Risk of Developing Alzheimer’s Disease: A New Computational Approach","year":2017,"lang":"en","type":"article","venue":"Processes","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; University of California, San Diego; Pfizer; Biogen; BioClinica; F. Hoffmann-La Roche; University of Southern California; Eli Lilly and Company; U.S. Department of Defense; Cure Alzheimer's Fund; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Bristol-Myers Squibb; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Crosstalk; Disease; Dementia; Computational biology; Biology; Neuroscience; Alzheimer's disease; Bioinformatics; Medicine; Pathology","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":[0.000926354,0.0006416879,0.000729357,0.00234747,0.0005755109,0.00148906,0.0007572655,0.0006673269,0.001491585],"category_scores_gemma":[0.003356221,0.0003683413,0.001651497,0.001550959,0.0004626398,0.0008308953,0.001120755,0.0007261428,0.0001747093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007876134,"about_ca_system_score_gemma":0.001543118,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01614689,"about_ca_topic_score_gemma":0.02031908,"domain_scores_codex":[0.9996752,0.000137621,0.00001712089,0.0001028357,0.00004453996,0.00002261765],"domain_scores_gemma":[0.9984955,0.001150877,0.0001061677,0.0001083028,0.00007768327,0.00006146625],"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":[0.0003602308,0.0002484611,0.07045171,0.0001779175,0.0008017248,0.0003053221,0.000145378,0.877399,0.002033289,0.008021226,0.001334496,0.03872129],"study_design_scores_gemma":[0.00002174024,0.00004252772,0.005975037,0.00001209766,0.0000900232,0.00008316644,0.00004790279,0.9789221,0.0001821824,0.01377938,0.0008318595,0.00001193861],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5540812,0.001722797,0.4288732,0.002688245,0.00006820705,0.0001493153,0.00708642,0.001926929,0.003403805],"genre_scores_gemma":[0.8537383,0.0008158777,0.1373899,0.0004504849,0.00005990419,0.0001837614,0.00597866,0.00008383654,0.001299264],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01614689,"threshold_uncertainty_score":0.0321058,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02074866908382436,"score_gpt":0.2565295997541235,"score_spread":0.2357809306702991,"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."}}