{"id":"W2989634691","doi":"10.1093/bioinformatics/btaa500","title":"iSOM-GSN: an integrative approach for transforming multi-omic data into gene similarity networks via self-organizing maps","year":2020,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Dimensionality reduction; Convolutional neural network; Graph; Euclidean distance; Visualization; Grid; Similarity (geometry); Artificial intelligence; Data mining; Pattern recognition (psychology); Theoretical computer science; Mathematics; Image (mathematics)","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":[0.0004180021,0.0004315951,0.000405212,0.00004480939,0.00031217,0.0001441627,0.001114665,0.0004478835,0.00000491546],"category_scores_gemma":[0.00006063053,0.0003844633,0.0001380329,0.000175004,0.00009116224,0.00009537421,0.0004567628,0.0003230054,0.000008163599],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004004157,"about_ca_system_score_gemma":0.0001412374,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001306048,"about_ca_topic_score_gemma":0.00002891233,"domain_scores_codex":[0.9980034,0.00003779671,0.0008310535,0.000433394,0.0001678647,0.0005264858],"domain_scores_gemma":[0.9983097,0.00002294797,0.0002911746,0.0009016524,0.0001334195,0.0003411079],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002808577,0.003103293,0.001655926,0.007110833,0.005165156,0.00001483345,0.128318,0.0926051,0.1590819,0.002147025,0.1232357,0.4747537],"study_design_scores_gemma":[0.001250019,0.0004032064,0.00001287912,0.0000147321,0.00009598103,0.00001430038,0.002004402,0.971212,0.005422442,0.00004223551,0.01898774,0.0005400309],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005487882,0.0005268738,0.991931,0.0001788021,0.0001749105,0.001035753,0.0002786206,0.00008825276,0.0002978705],"genre_scores_gemma":[0.2290803,0.0004581597,0.7537576,0.003199501,0.0008193208,0.00004652089,0.01253687,0.00007916371,0.000022595],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8786069,"threshold_uncertainty_score":0.9998607,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02975511432880835,"score_gpt":0.2581663139303418,"score_spread":0.2284111996015334,"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."}}