{"id":"W4388961251","doi":"10.1101/2023.11.22.568376","title":"scCross: A Deep Generative Model for Unifying Single-cell Multi-omics with Seamless Integration, Cross-modal Generation, and In-silico Exploration","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute; McGill University Health Centre; McGill University","funders":"National Key Research and Development Program of China; Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Fonds de Recherche du Québec - Santé; Shandong University","keywords":"Data integration; Computer science; Omics; In silico; Modalities; Data science; Artificial intelligence; Data mining; Bioinformatics; Biology","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":[0.001479446,0.0007019085,0.0005694097,0.0003868089,0.00034446,0.00101135,0.00161886,0.001294264,0.002484316],"category_scores_gemma":[0.002223204,0.0006499199,0.001167017,0.0003758922,0.001341166,0.0009319157,0.002199377,0.002059083,0.0007161549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009579792,"about_ca_system_score_gemma":0.001195236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00531437,"about_ca_topic_score_gemma":0.007942754,"domain_scores_codex":[0.9996763,0.000102589,0.00001081698,0.00009189226,0.00009251798,0.00002590836],"domain_scores_gemma":[0.9992224,0.0005180496,0.00005094541,0.00008556571,0.00006332693,0.0000596135],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003665902,0.00002050453,0.0004665183,0.00004161875,0.00005663011,0.00007461706,0.00004429527,0.9610599,0.00507591,0.01818098,0.001762222,0.01318008],"study_design_scores_gemma":[0.000002478467,0.000005418274,0.00002922685,0.000003037432,0.000002914839,0.000009957238,0.000002344262,0.9916459,0.0008070107,0.00683463,0.0006534783,0.00000371516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005782983,0.0001214183,0.9913319,0.0002393471,0.00004203572,0.00002324188,0.0002001056,0.001242246,0.001016748],"genre_scores_gemma":[0.4440804,0.0005089535,0.538613,0.001037331,0.0001122093,0.0004040976,0.001950777,0.001560648,0.01173261],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00531437,"threshold_uncertainty_score":0.01056689,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05709065548695007,"score_gpt":0.260405609179331,"score_spread":0.203314953692381,"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."}}