{"id":"W2808651428","doi":"10.1016/j.neuroimage.2018.06.028","title":"Multimodal imaging-based therapeutic fingerprints for optimizing personalized interventions: Application to neurodegeneration","year":2018,"lang":"en","type":"article","venue":"NeuroImage","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":42,"is_retracted":false,"has_abstract":false,"ca_institutions":"Montreal Neurological Institute and Hospital","funders":"National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Genentech; National Institutes of Health; Eisai; Servier; U.S. Department of Defense; Eli Lilly and Company; Lundbeckfonden; Alzheimer's Disease Neuroimaging Initiative; GE Healthcare; DoD Alzheimer's Disease Neuroimaging Initiative; Pfizer; BioClinica; Biogen; Novartis Pharmaceuticals Corporation; Bristol-Myers Squibb; Roche; Merck; Alzheimer's Drug Discovery Foundation; Meso Scale Diagnostics; Johnson and Johnson; Takeda Pharmaceutical Company; AbbVie; Fujirebio Europe; Alzheimer's Association","keywords":"Neuroimaging; Psychological intervention; Modalities; Context (archaeology); Intervention (counseling); Personalized medicine; Disease; Medicine; Population; Biomarker; Neuroscience; Psychology; Bioinformatics; Pathology; Psychiatry; Biology","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":[0.0006687042,0.0006280371,0.0006237764,0.001132214,0.000247982,0.001246087,0.0005018129,0.0008211796,0.002449975],"category_scores_gemma":[0.002812582,0.0002534423,0.000425752,0.001152309,0.0003944045,0.0007379692,0.0005677795,0.0007211256,0.000558596],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005440546,"about_ca_system_score_gemma":0.0006284039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00132314,"about_ca_topic_score_gemma":0.001612932,"domain_scores_codex":[0.9998257,0.00006980113,0.00000753056,0.00004626928,0.00003552247,0.00001527869],"domain_scores_gemma":[0.9995413,0.0002023628,0.0001043281,0.00004280008,0.00007896079,0.00003015316],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001407081,0.0005530485,0.01836276,0.000933857,0.0003997193,0.0006843434,0.0002309378,0.1030856,0.1772082,0.02021899,0.01193493,0.6649805],"study_design_scores_gemma":[0.0002086938,0.0007608423,0.02683538,0.0003155943,0.0007069142,0.0015836,0.0003644873,0.7467681,0.1257636,0.08073311,0.01578574,0.0001740283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2059177,0.0144874,0.7586589,0.004474913,0.0002064277,0.000233674,0.002851854,0.002113982,0.01105514],"genre_scores_gemma":[0.755995,0.006533083,0.2326446,0.000659316,0.0001951968,0.0002295068,0.0006304649,0.0003042012,0.002808508],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002449975,"threshold_uncertainty_score":0.008196056,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01945447648220032,"score_gpt":0.2960529049642969,"score_spread":0.2765984284820965,"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."}}