{"id":"W4282913188","doi":"10.1093/bib/bbac207","title":"Heterogeneous data integration methods for patient similarity networks","year":2022,"lang":"en","type":"review","venue":"Briefings in Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Biotechnology and Biological Sciences Research Council; Joint Research Centre; Medical Research Council; Horizon 2020 Framework Programme; National Institutes of Health; Directorate for Biological Sciences; Medical Research Council Canada; Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro; University of Toronto; Royal Holloway, University of London; Università degli Studi di Milano; Fundação Getulio Vargas; European Commission; Dartmouth College; Consejo Nacional de Ciencia y Tecnología; National Science Foundation","keywords":"Computer science; Leverage (statistics); Machine learning; Artificial intelligence; Data integration; Construct (python library); Data type; Similarity (geometry); Precision medicine; Data science; Data mining; Medicine; Image (mathematics)","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001487703,0.0005841092,0.001132593,0.0001550494,0.0002052917,0.0001416576,0.001310962,0.0007157042,0.00003661843],"category_scores_gemma":[0.0003340633,0.0005381666,0.000400968,0.0002465453,0.00007855831,0.00002229389,0.001765779,0.0006019303,0.000004054494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001183639,"about_ca_system_score_gemma":0.000297356,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001913605,"about_ca_topic_score_gemma":0.0000248964,"domain_scores_codex":[0.9968242,0.0001739455,0.001761176,0.0004927797,0.0001723675,0.0005755393],"domain_scores_gemma":[0.9969507,0.0002069193,0.001024083,0.00165353,0.00005286744,0.000111927],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001594645,0.00003969322,1.320031e-7,0.002139961,0.0001080908,8.532669e-7,0.00008537294,0.0005329395,2.699131e-7,0.00005970711,0.009350104,0.9876669],"study_design_scores_gemma":[0.0002194033,0.0001970054,5.86469e-8,0.0005505623,0.000179654,0.00007672707,0.00004012406,0.1165486,0.000003313787,0.00007001579,0.8815838,0.0005308077],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[5.236941e-7,0.6770257,0.3203571,0.00001591101,0.0004739208,0.001347568,0.0004419292,0.00001816069,0.0003191178],"genre_scores_gemma":[0.000001228815,0.8259535,0.1562733,0.0008587231,0.0001826208,0.0002823421,0.01634066,0.00006990424,0.00003766543],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9871361,"threshold_uncertainty_score":0.999707,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07406807324832056,"score_gpt":0.3697286143620437,"score_spread":0.2956605411137232,"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."}}