{"id":"W4235497171","doi":"10.1515/jib-2008-97","title":"Visual Comparison of Multiple Gene Expression Datasets in a Genomic Context","year":2008,"lang":"en","type":"article","venue":"Berichte aus der medizinischen Informatik und Bioinformatik/Journal of integrative bioinformatics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Genome Canada; Genome Alberta; University of Calgary","keywords":"Context (archaeology); Genome; Expression (computer science); Computational biology; Computer science; Function (biology); Microarray databases; Representation (politics); Gene; Visualization; Gene expression; Microarray analysis techniques; Data mining; Biology; Genetics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007213198,0.0005418648,0.0009621987,0.0007309662,0.0001550465,0.00005825672,0.0008450259,0.0003730371,0.00003907091],"category_scores_gemma":[0.0006306408,0.0003841708,0.0002874668,0.0006518262,0.0004110756,0.0004107186,0.0003002405,0.0005507493,0.0000344923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001092146,"about_ca_system_score_gemma":0.0007843266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001674608,"about_ca_topic_score_gemma":0.00002795514,"domain_scores_codex":[0.9946823,0.000104303,0.003695227,0.0001686655,0.0008565265,0.0004930303],"domain_scores_gemma":[0.9947633,0.0001150008,0.003430315,0.000537625,0.0008454838,0.0003082516],"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":[0.004935364,0.001831963,0.07553126,0.001187472,0.001438116,0.00003177863,0.1256218,0.002144187,0.6134914,0.000276114,0.1048351,0.06867547],"study_design_scores_gemma":[0.00582259,0.002171819,0.007792362,0.0005691197,0.0001086026,0.0004258235,0.03002682,0.01522457,0.8609245,0.00001807214,0.07607921,0.0008365075],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9372049,0.002781226,0.05483069,0.000094491,0.0007379074,0.000859916,0.0003159928,0.00002476537,0.003150129],"genre_scores_gemma":[0.9601038,0.0009389772,0.03752508,0.0004162918,0.0001263677,0.00002516251,0.0007889032,0.00002729818,0.0000480986],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2474332,"threshold_uncertainty_score":0.999861,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02669540576681016,"score_gpt":0.3142199920253397,"score_spread":0.2875245862585295,"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."}}