دورية أكاديمية
Convolutional neural network in proteomics and metabolomics for determination of comorbidity between cancer and schizophrenia.
| العنوان: | Convolutional neural network in proteomics and metabolomics for determination of comorbidity between cancer and schizophrenia. |
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| المؤلفون: | Kopylov AT; Biobanking Group, Branch of Institute of Biomedical Chemistry 'Scientific and Education Center,' 10 Pogodinskaya str., 119121 Moscow, Russian Federation. Electronic address: a.t.kopylov@gmail.com., Petrovsky DV; Biobanking Group, Branch of Institute of Biomedical Chemistry 'Scientific and Education Center,' 10 Pogodinskaya str., 119121 Moscow, Russian Federation., Stepanov AA; Biobanking Group, Branch of Institute of Biomedical Chemistry 'Scientific and Education Center,' 10 Pogodinskaya str., 119121 Moscow, Russian Federation., Rudnev VR; Biobanking Group, Branch of Institute of Biomedical Chemistry 'Scientific and Education Center,' 10 Pogodinskaya str., 119121 Moscow, Russian Federation., Malsagova KA; Biobanking Group, Branch of Institute of Biomedical Chemistry 'Scientific and Education Center,' 10 Pogodinskaya str., 119121 Moscow, Russian Federation., Butkova TV; Biobanking Group, Branch of Institute of Biomedical Chemistry 'Scientific and Education Center,' 10 Pogodinskaya str., 119121 Moscow, Russian Federation., Zakharova NV; N.A.Alekseev 1(st) Clinical Hospital of Psychiatry, Moscow Healthcare Department, 2 Zagorodnoe road, 115119, Russian Federation., Kostyuk GP; N.A.Alekseev 1(st) Clinical Hospital of Psychiatry, Moscow Healthcare Department, 2 Zagorodnoe road, 115119, Russian Federation., Kulikova LI; Institute of Mathematical Problems of Biology RAS-the Branch of Keldysh Institute of Applied Mathematics of Russian Academy of Sciences, 3 Institutskaya str., 142290 Pushchino, Moscow Region, Russian Federation., Enikeev DV; Institute of Urology and Reproductive Health, Sechenov University, 2/1 Bolshaya Pirogovskaya str., 119435 Moscow, Russian Federation., Potoldykova NV; Institute of Urology and Reproductive Health, Sechenov University, 2/1 Bolshaya Pirogovskaya str., 119435 Moscow, Russian Federation., Kulikov DA; M.F. Vladimirsky Moscow Regional Research and Clinical Institute, 61/2 Schepkina str., 129110 Moscow, Russian Federation., Zulkarnaev AB; M.F. Vladimirsky Moscow Regional Research and Clinical Institute, 61/2 Schepkina str., 129110 Moscow, Russian Federation., Kaysheva AL; Biobanking Group, Branch of Institute of Biomedical Chemistry 'Scientific and Education Center,' 10 Pogodinskaya str., 119121 Moscow, Russian Federation. |
| المصدر: | Journal of biomedical informatics [J Biomed Inform] 2021 Oct; Vol. 122, pp. 103890. Date of Electronic Publication: 2021 Aug 23. |
| نوع المنشور: | Journal Article; Research Support, Non-U.S. Gov't |
| اللغة: | English |
| بيانات الدورية: | Publisher: Elsevier Country of Publication: United States NLM ID: 100970413 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1532-0480 (Electronic) Linking ISSN: 15320464 NLM ISO Abbreviation: J Biomed Inform Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: Orlando : Elsevier Original Publication: San Diego, CA : Academic Press, c2001- |
| مواضيع طبية MeSH: | Neoplasms*/epidemiology , Schizophrenia*/epidemiology, Comorbidity ; Humans ; Male ; Metabolomics ; Neural Networks, Computer ; Proteomics |
| مستخلص: | The association between cancer risk and schizophrenia is widely debated. Despite many epidemiological studies, there is still no strong evidence regarding the molecular basis for the comorbidity between these two pathological conditions. The vast majority of assays have been performed using clinical records of schizophrenic patients or those undergoing cancer treatment and monitored for sufficient time to find shared features between the considered conditions. We performed mass spectrometry-based proteomic and metabolomic investigations of patients with different cancer phenotypes (breast, ovarian, renal, and prostate) and patients with schizophrenia. The resulting vast quantity of proteomic and metabolomic data were then processed using systems biology and one-dimensional (1D) convolutional neural network (1DCNN) machine learning approaches. Traditional systematic approaches permit the segregation of schizophrenia and cancer phenotypes on the level of biological processes, while 1DCNN recognized "signatures" that could segregate distinct cancer phenotypes and schizophrenia at the comorbidity level. The designed network efficiently discriminated unrelated pathologies with a model accuracy of 0.90 and different subtypes of oncophenotypes with an accuracy of 0.94. The proposed strategy integrates systematic analysis of identified compounds and application of 1DCNN model for unidentified ones to reveal the similarity between distinct phenotypes. (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.) |
| فهرسة مساهمة: | Keywords: Cancer; Comorbidity; Epigenetic factors; Neural network; Schizophrenia |
| تواريخ الأحداث: | Date Created: 20210826 Date Completed: 20211020 Latest Revision: 20211020 |
| رمز التحديث: | 20240628 |
| DOI: | 10.1016/j.jbi.2021.103890 |
| PMID: | 34438071 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1532-0480 |
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| DOI: | 10.1016/j.jbi.2021.103890 |