{"id":"W4404404222","doi":"10.1101/2024.11.12.623336","title":"scMoE: single-cell Multi-Modal Multi-Task Learning via Sparse Mixture-of-Experts","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Modal; Task (project management); Computer science; Multi-task learning; Artificial intelligence; Chemistry; Engineering; Systems engineering; Polymer chemistry","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":[0.002409076,0.001731945,0.001634253,0.0006719918,0.0004812422,0.001074648,0.003006781,0.0028432,0.003408262],"category_scores_gemma":[0.004452787,0.0009347132,0.001729488,0.0006579217,0.001083699,0.00124935,0.002645773,0.003359173,0.00118939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008973847,"about_ca_system_score_gemma":0.001447827,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008686011,"about_ca_topic_score_gemma":0.01065307,"domain_scores_codex":[0.9991392,0.0002835258,0.0000284035,0.0002624845,0.0001818361,0.0001045444],"domain_scores_gemma":[0.9985865,0.0008133642,0.00008628456,0.0001320232,0.0002675364,0.0001142561],"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":[0.0001813765,0.000134392,0.0008243664,0.0001505403,0.0002510462,0.0001440565,0.0000827261,0.8869041,0.005402783,0.004614415,0.007300355,0.09400994],"study_design_scores_gemma":[0.000006177059,0.00001400474,0.00005175324,0.000003742519,0.000004361441,0.000009092288,0.00000364491,0.9974558,0.0004415307,0.001704299,0.0003013098,0.000004402021],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009159384,0.0005736061,0.98681,0.0003933429,0.00008400191,0.00006829157,0.0001927227,0.001753136,0.0009655637],"genre_scores_gemma":[0.4390774,0.0004839207,0.5461467,0.001608115,0.0002670976,0.0004982199,0.002355387,0.0006094762,0.008953674],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008686011,"threshold_uncertainty_score":0.01727092,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01757847579605136,"score_gpt":0.2219249404461912,"score_spread":0.2043464646501399,"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."}}