{"id":"W2951879533","doi":"10.1186/s13104-018-3507-2","title":"GrapHi-C: graph-based visualization of Hi-C datasets","year":2018,"lang":"en","type":"article","venue":"BMC Research Notes","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Visualization; Graph; Computer science; Computational biology; Data mining; Biology; Theoretical computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008848723,0.00008620451,0.00009712344,0.0001461714,0.0001222618,0.00002391411,0.0002677585,0.0001185814,0.00004200354],"category_scores_gemma":[0.0003172227,0.00007679459,0.00005879806,0.0003184384,0.0004361001,0.000004408128,0.0001390433,0.00007392702,0.00002111741],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005260688,"about_ca_system_score_gemma":0.0001270098,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005704016,"about_ca_topic_score_gemma":0.0001924138,"domain_scores_codex":[0.998919,0.0001170869,0.0002138365,0.0001948942,0.0002678514,0.0002873651],"domain_scores_gemma":[0.9990215,0.00007368088,0.00006405803,0.0004727322,0.0002885594,0.00007950584],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000970415,0.0004753972,0.05297228,0.0004669157,0.0001476748,0.00000228179,0.0001988821,0.0003502202,0.824809,0.01007419,0.09502614,0.01450666],"study_design_scores_gemma":[0.001342569,0.00169483,0.009642107,0.00007957404,0.0000153886,0.000003395611,0.00008038429,0.008369072,0.9182125,0.003986175,0.0562194,0.0003546097],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8432682,0.0009638967,0.1518463,0.0001643508,0.0002695968,0.0006717117,0.0004181397,0.00002424509,0.002373609],"genre_scores_gemma":[0.9963193,0.00006635264,0.002225112,0.00007811284,0.0002643117,0.00001198694,0.0009831581,0.00001353895,0.00003811808],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1530512,"threshold_uncertainty_score":0.3131593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08039961376908901,"score_gpt":0.3956879777125182,"score_spread":0.3152883639434292,"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."}}