{"id":"W1964082988","doi":"10.1007/s10969-009-9071-1","title":"Structural genomics target selection for the New York consortium on membrane protein structure","year":2009,"lang":"en","type":"article","venue":"Journal of Structural and Functional Genomics","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"Structural Genomics Consortium","funders":"Genentech; National Institutes of Health; National Institute of General Medical Sciences; Magyar Tudományos Akadémia; Broad Institute; York University; University of Wisconsin-Madison","keywords":"Structural genomics; Transmembrane protein; Computational biology; Pipeline (software); Transmembrane domain; Biology; Genome; Membrane protein; Genomics; Selection (genetic algorithm); Protein structure; Bioinformatics; Genetics; Computer science; Gene; Biochemistry; Artificial intelligence; Membrane","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002513488,0.001059931,0.001167888,0.001250579,0.001473949,0.00105837,0.0009061469,0.0007107354,0.007856937],"category_scores_gemma":[0.003707802,0.0006422171,0.0009585813,0.001784078,0.0003321033,0.0006567133,0.001127857,0.001282135,0.004650971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001097037,"about_ca_system_score_gemma":0.002416815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003650389,"about_ca_topic_score_gemma":0.007942742,"domain_scores_codex":[0.99849,0.0002328889,0.0001381167,0.0002974868,0.0006714427,0.0001701061],"domain_scores_gemma":[0.9978257,0.0005331791,0.0002064321,0.000374617,0.0008554953,0.0002046105],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.002223093,0.0003645483,0.008313504,0.0006084198,0.0001100031,0.0005714968,0.0003649441,0.001658826,0.8558427,0.003429021,0.04330334,0.08321004],"study_design_scores_gemma":[0.0004654352,0.0009154142,0.02636111,0.00007115257,0.0001794284,0.001330015,0.0002198979,0.03608824,0.8108611,0.00204508,0.1213277,0.0001354125],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4036452,0.002349365,0.4993311,0.001908437,0.0004712125,0.006703929,0.04523547,0.02264917,0.01770628],"genre_scores_gemma":[0.2147888,0.001394036,0.5833809,0.001082478,0.0001567952,0.008766093,0.1684512,0.005454405,0.01652538],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007856937,"threshold_uncertainty_score":0.02628404,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009241040380548059,"score_gpt":0.2125520499586274,"score_spread":0.2033110095780794,"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."}}