{"id":"W4396703916","doi":"10.1002/advs.202306770","title":"Beaconet: A Reference‐Free Method for Integrating Multiple Batches of Single‐Cell Transcriptomic Data in Original Molecular Space","year":2024,"lang":"en","type":"article","venue":"Advanced Science","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Xidian University; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Feature vector; Data mining; Data integration; Feature (linguistics); Artificial intelligence","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":[0.000692341,0.0001777136,0.0002027545,0.0001228378,0.00006583268,0.00005394149,0.001059682,0.00008704253,0.000003160778],"category_scores_gemma":[0.0003616364,0.0001641688,0.00006187311,0.0004269885,0.0002569606,0.00004544075,0.0001266134,0.0001382004,8.840236e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003625203,"about_ca_system_score_gemma":0.0002618676,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000134008,"about_ca_topic_score_gemma":0.0005255957,"domain_scores_codex":[0.9983146,0.00003677959,0.0002946666,0.000791479,0.0002056773,0.000356803],"domain_scores_gemma":[0.9989285,0.00008854538,0.00005957874,0.0007565546,0.00009276291,0.00007398568],"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":[0.00008988919,0.00006475655,0.0001630509,0.00009955718,0.000006891738,0.000003213896,0.0001941941,0.0004338638,0.989222,0.0005843702,0.00002324925,0.009114899],"study_design_scores_gemma":[0.0006820291,0.0002898153,0.00004216512,0.0001027747,0.00001648392,0.000005006175,0.0002285089,0.0202745,0.9693272,0.0005781885,0.008247796,0.0002055238],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6035443,0.001822168,0.3931757,0.0001250193,0.0002738229,0.0002870598,0.0001614902,0.00002025274,0.0005902085],"genre_scores_gemma":[0.7913848,0.00004812166,0.2082924,0.00005033982,0.00002953581,0.00001727956,0.0000728475,0.00001876179,0.00008593384],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1878405,"threshold_uncertainty_score":0.6694608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03713350949643995,"score_gpt":0.318950048161713,"score_spread":0.281816538665273,"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."}}