{"id":"W2401261467","doi":"10.1007/978-1-60761-198-1_33","title":"Computational Modeling of Signaling Networks for Eukaryotic Chemosensing","year":2009,"lang":"en","type":"article","venue":"Methods in molecular biology","topic":"T-cell and B-cell Immunology","field":"Immunology and Microbiology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Hospitalier de l’Université de Montréal","funders":"National Institutes of Health","keywords":"Cell signaling; Computational model; Eukaryotic cell; Computer science; Signal transduction; Computational biology; Intracellular; GTPase; Extracellular; Cell biology; Biology; Artificial intelligence; Cell; Biochemistry","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":[0.0004294229,0.0005754525,0.0007552246,0.0004573691,0.0006910382,0.001092643,0.001106219,0.001375442,0.002557215],"category_scores_gemma":[0.00131445,0.0005076835,0.001000623,0.0005799444,0.000878055,0.001161737,0.0007862808,0.0007984769,0.0002757732],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001356027,"about_ca_system_score_gemma":0.001353906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00715891,"about_ca_topic_score_gemma":0.004167631,"domain_scores_codex":[0.9998356,0.0000609122,0.000009278904,0.00002872532,0.00004338899,0.00002206744],"domain_scores_gemma":[0.9996142,0.000265241,0.00003427128,0.00002369125,0.00003316615,0.00002933884],"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":[0.00000970422,0.00000493675,0.0001051468,0.00001041944,0.000004555947,0.00001506079,0.00001473303,0.9868041,0.0003773881,0.01181994,0.0001036332,0.0007303677],"study_design_scores_gemma":[0.000003309977,0.000002051377,0.00002086584,0.000001212937,0.000001389129,0.000003220386,0.000003032569,0.9962202,0.0001133041,0.003318397,0.0003116231,0.000001424885],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1343501,0.0006622181,0.8418511,0.001282181,0.00008610623,0.0001063405,0.0009065634,0.001090444,0.01966505],"genre_scores_gemma":[0.8179232,0.001169254,0.1702761,0.0002102828,0.00005821216,0.0005326559,0.0007863744,0.0001792702,0.008864621],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00715891,"threshold_uncertainty_score":0.01423448,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02593872584596166,"score_gpt":0.3338661125278554,"score_spread":0.3079273866818937,"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."}}