{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009808719,0.0000940147,0.0001076334,0.00002020328,0.0001560058,0.00005750365,0.0001341715,0.00005629423,0.00000131744],"category_scores_gemma":[0.00008320076,0.000088142,0.00001752008,0.00002691159,0.00007638305,0.00001092806,0.0001838742,0.00004373251,6.101855e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005851249,"about_ca_system_score_gemma":0.000226048,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000282846,"about_ca_topic_score_gemma":0.00003461258,"domain_scores_codex":[0.9994632,0.000008640035,0.0001663383,0.0001650423,0.00006500166,0.0001317788],"domain_scores_gemma":[0.9995069,0.00000574616,0.0002194093,0.0001527831,0.00005376995,0.00006137756],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002337207,0.0004403303,0.7282258,0.008370249,0.0009019169,0.0000197709,0.004380561,0.003766012,0.1472482,0.0008372248,0.0007650547,0.1027076],"study_design_scores_gemma":[0.004314608,0.0002274723,0.6777284,0.0005562178,0.0001569644,0.00004288366,0.0001531571,0.01089269,0.2861363,0.007693011,0.01073871,0.001359554],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9870141,0.005528697,0.006851216,0.0000868451,0.00001864844,0.0002264196,0.00002863332,0.000003768105,0.0002417296],"genre_scores_gemma":[0.9880303,0.0004188365,0.01123325,0.00004079882,0.00006517735,0.00001797617,0.0001063774,0.000009174069,0.00007810964],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1388881,"threshold_uncertainty_score":0.3594326,"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."}}