دورية أكاديمية
أعرض في EDS

An effective deep learning-based approach for splice site identification in gene expression.

التفاصيل البيبلوغرافية
العنوان: An effective deep learning-based approach for splice site identification in gene expression.
المؤلفون: Ali M; Department of Computer Science, Bacha Khan University, Charsadda, KP, Pakistan., Shah D; Department of Computer Science, Bacha Khan University, Charsadda, KP, Pakistan., Qazi S; Department of Computer Science, Bacha Khan University, Charsadda, KP, Pakistan., Khan IA; Department of Computer Science, Bacha Khan University, Charsadda, KP, Pakistan., Abrar M; Faculty of Computer Science, Arab Open University, Muscat, Oman, Sultanate of Oman., Zahir S; Institute of Computer Sciences and Information Technology, The University of Agriculture Peshawar, Peshawar, KP, Pakistan.
المصدر: Science progress [Sci Prog] 2024 Jul-Sep; Vol. 107 (3), pp. 368504241266588.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: SAGE Publications Country of Publication: England NLM ID: 0411361 Publication Model: Print Cited Medium: Internet ISSN: 2047-7163 (Electronic) Linking ISSN: 00368504 NLM ISO Abbreviation: Sci Prog Subsets: MEDLINE
أسماء مطبوعة: Publication: <2019-> : [London] : SAGE Publications
Original Publication: Oxford, Blackwell Scientific Publications [etc.]
مواضيع طبية MeSH: Deep Learning* , RNA Splice Sites*/genetics , Neural Networks, Computer*, Humans ; RNA Splicing ; Introns/genetics ; RNA, Messenger/genetics ; Algorithms ; Gene Expression ; Computational Biology/methods ; Exons/genetics
مستخلص: A crucial stage in eukaryote gene expression involves mRNA splicing by a protein assembly known as the spliceosome. This step significantly contributes to generating and properly operating the ultimate gene product. Since non-coding introns disrupt eukaryotic genes, splicing entails the elimination of introns and joining exons to create a functional mRNA molecule. Nevertheless, accurately finding splice sequence sites using various molecular biology techniques and other biological approaches is complex and time-consuming. This paper presents a precise and reliable computer-aided diagnosis (CAD) technique for the rapid and correct identification of splice site sequences. The proposed deep learning-based framework uses long short-term memory (LSTM) to extract distinct patterns from RNA sequences, enabling rapid and accurate point mutation sequence mapping. The proposed network employs one-hot encodings to find sequential patterns that effectively identify splicing sites. A thorough ablation study of traditional machine learning, one-dimensional convolutional neural networks (1D-CNNs), and recurrent neural networks (RNNs) models was conducted. The proposed LSTM network outperformed existing state-of-the-art approaches, improving accuracy by 3% and 2% for the acceptor and donor sites datasets.
Competing Interests: Declaration of conflicting interestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
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فهرسة مساهمة: Keywords: Artificial intelligence; RNA analysis; biomedical data; deep learning; genomics; splicing sites
المشرفين على المادة: 0 (RNA Splice Sites)
0 (RNA, Messenger)
تواريخ الأحداث: Date Created: 20240725 Date Completed: 20240725 Latest Revision: 20240728
رمز التحديث: 20240728
مُعرف محوري في PubMed: PMC11273556
DOI: 10.1177/00368504241266588
PMID: 39051530
قاعدة البيانات: MEDLINE
الوصف
تدمد:2047-7163
DOI:10.1177/00368504241266588