{"id":"W4393205186","doi":"10.1101/2024.03.19.585796","title":"NEST: Spatially-mapped cell-cell communication patterns using a deep learning-based attention mechanism","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":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Institute for Cancer Research; University of Toronto; Vector Institute; Princess Margaret Cancer Centre; University Health Network","funders":"","keywords":"Computer science; Transcriptome; Computational biology; Artificial intelligence; Benchmarking; Biology; Gene; Gene expression; Genetics","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":[0.0005002526,0.0005132912,0.0004501223,0.0004731904,0.0002663494,0.0005430637,0.001353831,0.001022195,0.001937899],"category_scores_gemma":[0.001255249,0.000297127,0.0005271836,0.0004178645,0.0004797653,0.0008649913,0.0012211,0.0009785159,0.0003469677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007929493,"about_ca_system_score_gemma":0.0004931072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004987042,"about_ca_topic_score_gemma":0.005341341,"domain_scores_codex":[0.9998541,0.00003034341,0.000004401891,0.00005093238,0.00003595306,0.00002418982],"domain_scores_gemma":[0.9996995,0.0001375023,0.00003834434,0.00003607064,0.00005287201,0.0000357135],"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":[0.0002878545,0.0001595205,0.002814065,0.0001037093,0.0001145046,0.0001984067,0.000083935,0.7646008,0.05085716,0.01124043,0.006593971,0.1629457],"study_design_scores_gemma":[0.00000460561,0.00001368338,0.0001489359,0.000001400707,0.000002868237,0.000009854634,0.000002656245,0.9953922,0.00197758,0.002259051,0.0001843385,0.000002889636],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1046975,0.0003462148,0.8880043,0.000580101,0.00009391643,0.00004689925,0.0003822261,0.003582291,0.002266548],"genre_scores_gemma":[0.8351608,0.0001677824,0.1583043,0.0003485592,0.00005918732,0.00009440236,0.0006124398,0.000209672,0.005042856],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004987042,"threshold_uncertainty_score":0.009916008,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.012186269623952,"score_gpt":0.2130063404584842,"score_spread":0.2008200708345322,"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."}}