{"id":"W2145362263","doi":"10.1093/hmg/ddq232","title":"Integrative gene–tissue microarray-based approach for identification of human disease biomarkers: application to amyotrophic lateral sclerosis","year":2010,"lang":"en","type":"article","venue":"Human Molecular Genetics","topic":"Amyotrophic Lateral Sclerosis Research","field":"Medicine","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Hôpital Notre-Dame","funders":"","keywords":"Amyotrophic lateral sclerosis; Biology; DNA microarray; Transcriptome; SOD1; Laser capture microdissection; Proteomics; Microarray; Computational biology; Gene; Genetics; Frontotemporal dementia; TARDBP; Gene expression; Disease; Pathology; Dementia; Medicine","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":[0.0006797453,0.0006813149,0.0008165563,0.0008799864,0.0003509551,0.0004284786,0.0004993251,0.0005898021,0.001078291],"category_scores_gemma":[0.0003743059,0.0003127902,0.0005427796,0.0008066972,0.0003030786,0.0001944351,0.0003241153,0.0008292561,0.0005899775],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004300317,"about_ca_system_score_gemma":0.0003667876,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004854061,"about_ca_topic_score_gemma":0.001010132,"domain_scores_codex":[0.9994228,0.0001226014,0.00003034574,0.0002193357,0.000151951,0.0000530339],"domain_scores_gemma":[0.9998272,0.00005125708,0.00003047602,0.00003073403,0.00003335454,0.00002699694],"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":[0.00007919434,0.00004215079,0.0003816071,0.00005887407,0.00001757592,0.00003453504,0.00001698012,0.000243234,0.9946905,0.0001093769,0.0001383168,0.004187685],"study_design_scores_gemma":[0.00005827411,0.001150922,0.03026741,0.00002136058,0.0001802159,0.001163682,0.00009506733,0.01861158,0.9356046,0.0008166532,0.01196457,0.00006577189],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6107836,0.007811496,0.3624026,0.001120849,0.0002964174,0.001118682,0.008641183,0.002608068,0.005217121],"genre_scores_gemma":[0.5682089,0.004395782,0.4143656,0.0005240836,0.0001002201,0.001978477,0.006200697,0.0001038969,0.004122223],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001078291,"threshold_uncertainty_score":0.003607213,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02762372032605147,"score_gpt":0.3213427137565373,"score_spread":0.2937189934304859,"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."}}