{"id":"W2798116182","doi":"10.1158/0008-5472.can-17-1383","title":"CrosstalkNet: A Visualization Tool for Differential Co-expression Networks and Communities","year":2018,"lang":"en","type":"article","venue":"Cancer Research","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Institute for Cancer Research; McGill University Health Centre; Princess Margaret Cancer Centre; University Health Network; University of Toronto; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; Government of Canada; Stand Up To Cancer Canada; Canadian Institutes of Health Research; Cancer Research Society","keywords":"DECIPHER; Visualization; Biological network; Laser capture microdissection; Computer science; Stromal cell; Bipartite graph; Complex network; Computational biology; Scale (ratio); Biology; Data mining; Bioinformatics; Gene expression; Theoretical computer science; Gene; Genetics; Cancer research; World Wide Web; Physics","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":[0.00130727,0.001567824,0.0006888368,0.005248316,0.0006347631,0.001450831,0.001081795,0.0008506402,0.01692145],"category_scores_gemma":[0.00376793,0.0005286938,0.0008745456,0.002648812,0.0003649345,0.001599593,0.002195021,0.001093937,0.002139597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007093939,"about_ca_system_score_gemma":0.0009603227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002999131,"about_ca_topic_score_gemma":0.004405004,"domain_scores_codex":[0.9994457,0.0001266226,0.00004765831,0.0001418659,0.0001898887,0.00004824999],"domain_scores_gemma":[0.9983231,0.000993853,0.0001874358,0.0001520517,0.0002096409,0.0001337495],"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":[0.002324001,0.000625178,0.02917628,0.003319618,0.000866519,0.0030432,0.003372256,0.07189974,0.09446644,0.04158112,0.318811,0.4305148],"study_design_scores_gemma":[0.0003343311,0.0001771538,0.01833241,0.0003290097,0.0001477228,0.001259352,0.0005831561,0.7353358,0.03982398,0.06011443,0.1433772,0.0001854353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04888532,0.0008031101,0.6412012,0.0009836181,0.0002671534,0.0002991126,0.05277459,0.2479953,0.00679059],"genre_scores_gemma":[0.3281372,0.0009979141,0.593812,0.000516472,0.0001318325,0.001448475,0.05631505,0.01238098,0.006260091],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01692145,"threshold_uncertainty_score":0.0566079,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0437639864106198,"score_gpt":0.3989783666803139,"score_spread":0.3552143802696941,"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."}}