{"id":"W4410861341","doi":"10.21203/rs.3.rs-6656477/v1","title":"scGALA: Graph Link Prediction Based Cell Alignment for Comprehensive Data Integration","year":2025,"lang":"en","type":"preprint","venue":"Research Square","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Alliance de recherche numérique du Canada","keywords":"Link (geometry); Computer science; Data integration; Graph; Data mining; Data science; Theoretical computer science; Computer network","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":[0.0009944428,0.002505584,0.001478511,0.004052147,0.001281901,0.002600331,0.002291203,0.001265964,0.02890547],"category_scores_gemma":[0.00445091,0.001124539,0.001854885,0.005082238,0.0005086501,0.0024929,0.003090407,0.002449398,0.01616557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006090422,"about_ca_system_score_gemma":0.001311678,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003502708,"about_ca_topic_score_gemma":0.005993264,"domain_scores_codex":[0.9992979,0.0001034092,0.00004771947,0.0002747994,0.0002001039,0.00007597907],"domain_scores_gemma":[0.9987018,0.0004037184,0.00006073767,0.0005925726,0.0001516833,0.00008953857],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001471663,0.0003764829,0.006489126,0.002150615,0.001012784,0.0006387333,0.0006089407,0.02988482,0.05641129,0.02089091,0.4332271,0.4468375],"study_design_scores_gemma":[0.0005737545,0.0002118465,0.004999422,0.0002169021,0.000378026,0.0006556829,0.0003859857,0.6027471,0.0607336,0.09255465,0.2363544,0.0001886418],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01471716,0.000511641,0.5440065,0.0003728926,0.0003734552,0.0002943396,0.07231443,0.3632568,0.004152857],"genre_scores_gemma":[0.08327969,0.0005190928,0.6837734,0.0002061222,0.0001299268,0.0007102587,0.2013341,0.02544436,0.00460303],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02890547,"threshold_uncertainty_score":0.09669846,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08414829366927029,"score_gpt":0.3782562808640266,"score_spread":0.2941079871947563,"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."}}