{"id":"W4221044879","doi":"10.1186/s13059-022-02629-7","title":"TraSig: inferring cell-cell interactions from pseudotime ordering of scRNA-Seq data","year":2022,"lang":"en","type":"article","venue":"Genome biology","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University Health Centre","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of General Medical Sciences; National Heart, Lung, and Blood Institute; National Institute of Diabetes and Digestive and Kidney Diseases; National Cancer Institute; National Institutes of Health","keywords":"Biology; Inference; Computational biology; Software; Computer science; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001422512,0.0001539625,0.0002071543,0.00006649936,0.0001365939,0.000009677538,0.0006835632,0.00008828766,0.0004478736],"category_scores_gemma":[0.00001721135,0.0001679907,0.00007428983,0.0000942363,0.00007232332,0.000005381072,0.0006626587,0.0001941911,0.00001319724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002238346,"about_ca_system_score_gemma":0.00008632106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006485584,"about_ca_topic_score_gemma":0.0001298619,"domain_scores_codex":[0.998796,0.0001061623,0.0002998791,0.0004851117,0.00006245969,0.0002503602],"domain_scores_gemma":[0.9990587,0.00002721825,0.0001251895,0.0006978732,0.00003255976,0.00005846506],"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":[0.00006985029,0.0001511046,0.006186794,0.00001126557,0.00004942305,0.000001998414,0.0001050282,0.0003950949,0.9920418,0.00001172163,0.0001719277,0.000803981],"study_design_scores_gemma":[0.00198392,0.0009322538,0.005046431,0.000007202985,0.0001063658,0.00002838549,0.0006095372,0.001078476,0.5954214,0.0001972273,0.3938732,0.0007154988],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.990583,0.001527037,0.004363004,0.00006280364,0.0006178422,0.0001260253,0.0008823626,0.00001694539,0.001820949],"genre_scores_gemma":[0.9944646,0.0001358888,0.001714925,0.0001643316,0.0002293378,0.00001300216,0.002902152,0.00002714717,0.0003486539],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3966204,"threshold_uncertainty_score":0.6850462,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03634110013414565,"score_gpt":0.2595889542510274,"score_spread":0.2232478541168817,"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."}}