{"id":"W4398781551","doi":"10.1101/2024.05.21.589311","title":"Cell Maps for Artificial Intelligence: AI-Ready Maps of Human Cell Architecture from Disease-Relevant Cell Lines","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Simon Fraser University","funders":"National Institutes of Health; University of Virginia","keywords":"Human cell; Architecture; Cell; Computer science; Human disease; Artificial intelligence; Disease; Computational biology; Cell culture; Biology; Geography; Medicine; Pathology; Genetics","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":[0.003354186,0.001013329,0.0008316506,0.002987087,0.0007850025,0.004247452,0.001633692,0.001131612,0.007397518],"category_scores_gemma":[0.008498468,0.0006244461,0.0009750588,0.002675618,0.001158551,0.002404707,0.003849616,0.002306203,0.006299939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009003008,"about_ca_system_score_gemma":0.001511434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001675635,"about_ca_topic_score_gemma":0.002384083,"domain_scores_codex":[0.9986909,0.0002940633,0.00008546303,0.000226365,0.0006369954,0.00006619451],"domain_scores_gemma":[0.995754,0.00119956,0.0002743549,0.001916641,0.0006145164,0.000240947],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005496733,0.0002183642,0.01222016,0.001495062,0.0004393279,0.000587291,0.001099217,0.04119977,0.07348076,0.2679944,0.2581813,0.3425347],"study_design_scores_gemma":[0.00007317722,0.00004996025,0.007484181,0.0002542808,0.00007873181,0.0004384619,0.0004637031,0.1155048,0.05444801,0.3372396,0.4838556,0.000109501],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01459168,0.00107011,0.9005018,0.002115239,0.000351815,0.0001340291,0.04385239,0.02513589,0.01224717],"genre_scores_gemma":[0.06948079,0.001549079,0.7860993,0.0004980905,0.0001637315,0.0005985334,0.1338844,0.003835568,0.003890519],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007397518,"threshold_uncertainty_score":0.02474719,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01233690393105322,"score_gpt":0.2547016649546653,"score_spread":0.2423647610236121,"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."}}