{"id":"W4404010966","doi":"10.1101/2024.11.02.621677","title":"Membrane mimetic thermal proteome profiling (MM-TPP) towards mapping membrane protein-ligand dynamic interactions","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina; University of British Columbia","funders":"","keywords":"Proteome; Profiling (computer programming); Membrane; Chemistry; Membrane protein; Computational biology; Biophysics; Cell biology; Biology; Computer science; 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":[0.0005453491,0.0006279459,0.0003984253,0.00040487,0.0002464393,0.000496906,0.000397569,0.0004698005,0.0009247697],"category_scores_gemma":[0.0004593469,0.0002908632,0.0003846025,0.0003550453,0.0003190876,0.0004922007,0.000692884,0.001143477,0.0005892231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002526724,"about_ca_system_score_gemma":0.0001990523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001455673,"about_ca_topic_score_gemma":0.0002415778,"domain_scores_codex":[0.9995862,0.00008076372,0.0000219198,0.0001126633,0.0001456864,0.00005280117],"domain_scores_gemma":[0.9997548,0.00005312838,0.00008353399,0.00003328858,0.00004310361,0.00003204483],"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":[0.00002790319,0.000009282015,0.0001030666,0.00003835013,0.000008654091,0.00001913817,0.000007463147,0.0001113388,0.9980251,0.0001228933,0.00009698859,0.001429834],"study_design_scores_gemma":[0.000003505467,0.00007454771,0.001151267,0.000004682524,0.00000962919,0.0001816053,0.00001426289,0.003381835,0.9927245,0.0001145949,0.002331058,0.00000864168],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6946081,0.002960827,0.2946571,0.0004926412,0.0001837645,0.0001955052,0.00223176,0.00159978,0.003070623],"genre_scores_gemma":[0.8590772,0.002524832,0.1314131,0.0005015865,0.00009313018,0.0004410237,0.00215299,0.0002798496,0.003516336],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.0009247697,"threshold_uncertainty_score":0.00309366,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009100101414967084,"score_gpt":0.2377960388189269,"score_spread":0.2286959374039598,"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."}}