{"id":"W3131580959","doi":"10.1155/2021/6653793","title":"[Retracted] Identification of Tumor Tissue of Origin with RNA‐Seq Data and Using Gradient Boosting Strategy","year":2021,"lang":"en","type":"article","venue":"BioMed Research International","topic":"Cancer Diagnosis and Treatment","field":"Medicine","cited_by":14,"is_retracted":true,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Science Foundation of Hainan Province; National Natural Science Foundation of China","keywords":"RNA-Seq; Boosting (machine learning); Gradient boosting; Computational biology; Identification (biology); Biology; Artificial intelligence; Computer science; Bioinformatics; Genetics; Transcriptome; Gene; Gene expression","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":{"nature":"Retraction","reason":"Compromised Peer Review;Investigation by Journal/Publisher;Investigation by Third Party;Paper Mill;Unreliable Results and/or Conclusions;","date":"11/29/2023 0:00","openalex_flagged":true},"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.004291485,0.002037524,0.001226245,0.001714158,0.0009436036,0.001655376,0.0033192,0.002314112,0.0337558],"category_scores_gemma":[0.01083626,0.001085366,0.002260648,0.001298791,0.0005742484,0.001185284,0.001425388,0.002943967,0.02869313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005941389,"about_ca_system_score_gemma":0.002104654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007381817,"about_ca_topic_score_gemma":0.01097289,"domain_scores_codex":[0.9984878,0.0003316733,0.0001060293,0.0004942809,0.0004193203,0.0001608513],"domain_scores_gemma":[0.9961476,0.0008772408,0.0001604477,0.0007639144,0.001816897,0.0002338542],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006305883,0.0002499967,0.007666663,0.001551453,0.0004574362,0.0005085614,0.0003182262,0.0198776,0.04818086,0.003156704,0.6426187,0.2747833],"study_design_scores_gemma":[0.0004215729,0.0003288551,0.01225657,0.0003989876,0.0003542537,0.0009931437,0.0001474966,0.4199988,0.08076181,0.01576817,0.4681605,0.000409807],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02567369,0.002392195,0.7647035,0.007660329,0.02139184,0.001349844,0.05424217,0.1063344,0.01625206],"genre_scores_gemma":[0.1012208,0.001686833,0.7686139,0.004850458,0.002426331,0.001619507,0.0689285,0.01312742,0.03752622],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9976859,"threshold_uncertainty_score":0.1129245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.247532381218903,"score_gpt":0.4835095380653701,"score_spread":0.2359771568464671,"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."}}