{"id":"W3172251064","doi":"10.12688/f1000research.52460.1","title":"scNetViz: from single cells to networks using Cytoscape","year":2021,"lang":"en","type":"preprint","venue":"F1000Research","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Centre for Phenogenomics; Princess Margaret Cancer Centre; University of Toronto; University Health Network","funders":"National Human Genome Research Institute; National Institute of General Medical Sciences; Chan Zuckerberg Initiative; Silicon Valley Community Foundation","keywords":"Python (programming language); Computational biology; Workflow; NS3; Computer science; Biology; Bioinformatics; Genetics; Programming language","routes":{"ca_aff":true,"ca_fund":false,"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":[0.002045234,0.002208332,0.002105203,0.003067891,0.001469093,0.00374659,0.005150492,0.002030835,0.1131413],"category_scores_gemma":[0.007773633,0.002016964,0.002845726,0.002348202,0.0006575176,0.002434157,0.003484734,0.004879831,0.03757469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001189427,"about_ca_system_score_gemma":0.003829157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007487488,"about_ca_topic_score_gemma":0.01189854,"domain_scores_codex":[0.9988185,0.0002051538,0.00009589798,0.0003256772,0.0004193116,0.0001354872],"domain_scores_gemma":[0.9979207,0.001122711,0.0001282827,0.0002892653,0.0002960468,0.0002430162],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005040799,0.0001348552,0.002260332,0.005890765,0.001036304,0.0005913005,0.0007198477,0.02236013,0.01134993,0.02317518,0.8601384,0.07183901],"study_design_scores_gemma":[0.0009612667,0.0001072984,0.00291869,0.0009638485,0.0003217592,0.0004029917,0.0001822453,0.1038569,0.02398532,0.08760066,0.7782821,0.0004169446],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"software","genre_gemma":"software","genre_scores_codex":[0.005926861,0.001545676,0.2305991,0.001468561,0.002276829,0.0006348856,0.2662338,0.4774899,0.01382447],"genre_scores_gemma":[0.05991308,0.003564579,0.3591444,0.004013472,0.0005951135,0.006882769,0.3983,0.1504489,0.01713788],"genre_candidate":"software","genre_consensus":"software","teacher_disagreement_score":0.1131413,"threshold_uncertainty_score":0.3784953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06112382289657715,"score_gpt":0.3032284086811335,"score_spread":0.2421045857845564,"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."}}