دورية أكاديمية
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MS-ACGAN: A modified auxiliary classifier generative adversarial network for schizophrenia's samples augmentation based on microarray gene expression data.

التفاصيل البيبلوغرافية
العنوان: MS-ACGAN: A modified auxiliary classifier generative adversarial network for schizophrenia's samples augmentation based on microarray gene expression data.
المؤلفون: Jahanyar B; Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran., Tabatabaee H; Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran. Electronic address: h_tabatabaee@mshdiau.ac.ir., Rowhanimanesh A; Department of Electrical Engineering, University of Neyshabur, Neyshabur, Iran.
المصدر: Computers in biology and medicine [Comput Biol Med] 2023 Aug; Vol. 162, pp. 107024. Date of Electronic Publication: 2023 May 26.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Elsevier Country of Publication: United States NLM ID: 1250250 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-0534 (Electronic) Linking ISSN: 00104825 NLM ISO Abbreviation: Comput Biol Med Subsets: MEDLINE
أسماء مطبوعة: Publication: New York : Elsevier
Original Publication: New York, Pergamon Press.
مواضيع طبية MeSH: Artificial Intelligence* , Schizophrenia*/diagnosis , Schizophrenia*/genetics, Humans ; Reproducibility of Results ; Microarray Analysis ; Gene Expression
مستخلص: Artificial intelligence-based models and robust computational methods have expedited the data-to-knowledge trajectory in precision medicine. Although machine learning models have been widely applied in medical data analysis, some barriers are yet to be challenging, such as available biosample shortage, prohibitive costs, rare diseases, and ethical considerations. Transcriptomics, an omics approach that studies gene activities and provides gene expression data such as microarray and RNA-Sequences faces the difficulties of biospecimen collection, particularly for mental disorders, as some psychiatric patients avoid medical care. Microarray data suffers from the low number of available samples, making it challenging to apply machine learning models. However, adversarial generative network (GAN), the hottest paradigm in deep learning, has created unprecedented momentum in data augmentation and efficiently expands datasets. This paper proposes a novel model termed MS-ACGAN, where the generator feeds on a bordered Gaussian distribution. In machine learning, calibration is of utmost importance, which gives insight into model uncertainty and is considered a crucial step toward improving the robustness and reliability of models. Therefore, we apply calibration techniques to classifiers and focus on estimating their probabilities as accurately as possible. Additionally, we present our trustworthy outputs by harnessing confidence intervals that confine the point estimate limitations and report a range of expected values for performance metrics. Both concepts statistically describe the implemented model's reliability in this study. Furthermore, we employ two quantitative measures, GAN-train and GAN-test, to demonstrate that the artificial data generated by our robust approach remarkably resembles the original data characteristics.
Competing Interests: Declaration of competing interest None Declared.
(Copyright © 2023. Published by Elsevier Ltd.)
فهرسة مساهمة: Keywords: Adversarial generative network; Confidence intervals; Data augmentation; Deep learning; Machine learning; Microarray gene expression; Omics data
تواريخ الأحداث: Date Created: 20230601 Date Completed: 20230619 Latest Revision: 20231121
رمز التحديث: 20240628
DOI: 10.1016/j.compbiomed.2023.107024
PMID: 37263150
قاعدة البيانات: MEDLINE
الوصف
تدمد:1879-0534
DOI:10.1016/j.compbiomed.2023.107024