{"id":"W2022211764","doi":"10.1109/tcbb.2009.51","title":"A General Framework for Analyzing Data from Two Short Time-Series Microarray Experiments","year":2009,"lang":"en","type":"article","venue":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; McGill University; McGill University Health Centre","funders":"Canadian Institutes of Health Research; Canada Research Chairs","keywords":"Series (stratigraphy); Computer science; Kernel (algebra); Time series; Transformation (genetics); Data mining; Independence (probability theory); Tensor (intrinsic definition); Data transformation; Algorithm; Theoretical computer science; Mathematics; Machine learning; Statistics; Gene; Biology","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":[],"consensus_categories":[],"category_scores_codex":[0.000098568,0.0001571032,0.000143039,0.000072083,0.0002285542,0.00003873209,0.0002832317,0.0001722822,0.00002535398],"category_scores_gemma":[0.00002379123,0.0001414488,0.00005624604,0.00007808459,0.00008151499,0.000023912,0.00001184982,0.00009581476,0.000009961944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001136102,"about_ca_system_score_gemma":0.00005717909,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002593663,"about_ca_topic_score_gemma":0.000002401921,"domain_scores_codex":[0.9991587,0.00003154389,0.0002873452,0.0002931391,0.00006969009,0.0001596208],"domain_scores_gemma":[0.9993013,0.00006005039,0.0000765512,0.0004232877,0.00007025836,0.00006853962],"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":[0.00142217,0.0004214435,0.0007681587,0.00003591345,0.0005716686,7.926806e-7,0.0005641197,0.01968434,0.6246731,0.001475474,0.006090511,0.3442923],"study_design_scores_gemma":[0.005137509,0.003348649,0.00493831,0.0001935534,0.0003464548,0.00006195194,0.00062564,0.2683631,0.5694839,0.09081534,0.05456574,0.002119932],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0562333,0.0002340903,0.9418601,0.000577723,0.0002326049,0.0001984781,0.0005847864,0.00002231687,0.00005658475],"genre_scores_gemma":[0.4586592,0.0001744905,0.5363014,0.001052467,0.0002453638,0.00002277729,0.003406646,0.000009970024,0.0001276886],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4055587,"threshold_uncertainty_score":0.5768116,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03797228068075513,"score_gpt":0.3451076156334334,"score_spread":0.3071353349526783,"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."}}