دورية أكاديمية
Splice site recognition - deciphering Exon-Intron transitions for genetic insights using Enhanced integrated Block-Level gated LSTM model.
| العنوان: | Splice site recognition - deciphering Exon-Intron transitions for genetic insights using Enhanced integrated Block-Level gated LSTM model. |
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| المؤلفون: | Sha M; Department of Software Engineering, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al Kharj 11942, Kingdom of Saudi Arabia. Electronic address: ms.mohamed@psau.edu.sa., Parveen Rahamathulla M; Department of Basic Medical Sciences, College of Medicine, Prince Sattam bin Abdulaziz University, Al Kharj 11942, Kingdom of Saudi Arabia. Electronic address: m.parveen@psau.edu.sa. |
| المصدر: | Gene [Gene] 2024 Jul 15; Vol. 915, pp. 148429. Date of Electronic Publication: 2024 Apr 03. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Elsevier/North-Holland Country of Publication: Netherlands NLM ID: 7706761 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-0038 (Electronic) Linking ISSN: 03781119 NLM ISO Abbreviation: Gene Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: Amsterdam, Elsevier/North-Holland, 1976- |
| مواضيع طبية MeSH: | Exons*/genetics , Introns*/genetics , RNA Splice Sites*, Humans ; Computational Biology/methods ; RNA Splicing ; Autism Spectrum Disorder/genetics ; Algorithms ; Deep Learning |
| مستخلص: | Bioinformatics is a contemporary interdisciplinary area focused on analyzing the growing number of genome sequences. Gene variants are differences in DNA sequences among individuals within a population. Splice site recognition is a crucial step in the process of gene expression, where the coding sequences of genes are joined together to form mature messenger RNA (mRNA). These genetic variants that disrupt genes are believed to be the primary reason for neuro-developmental disorders like ASD (Autism Spectrum Disorder) is a neuro-developmental disorder that is diagnosed in individuals, families, and society and occurs as the developmental delay in one among the hundred genes that are associated with these disorders. Missense variants, premature stop codons, or deletions alter both the quality and quantity of encoded proteins. Predicting genes within exons and introns presents main challenges, such as dealing with sequencing errors, short reads, incomplete genes, overlapping, and more. Although many traditional techniques have been utilized in creating an exon prediction system, the primary challenge lies in accurately identifying the length and spliced strand location classification of exons in conjunction with introns. From now on, the suggested approach utilizes a Deep Learning algorithm to analyze intricate and extensive genomic datasets. M-LSTM is utilized to categorize three binary combinations (EI as 1, IE as 2, and none as 3) using spliced DNA strands. The M-LSTM system is able to sequence extensive datasets, ensuring that long information can be stored without any impact on the current input or output. This enables it to recognize and address long-term connections and problems with rapidly increasing gradients. The proposed model is compared internally with Naïve Bayes and Random Forest to assess its efficacy. Additionally, the proposed model's performance is forecasted by utilizing probabilistic parameters like recall, F1-score, precision, and accuracy to assess the effectiveness of the proposed system. Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (Copyright © 2024 Elsevier B.V. All rights reserved.) |
| فهرسة مساهمة: | Keywords: Block gate; Deep learning; Divergent gate; Exon; Genome; Intron; LSTM; Merge gate; Mutations; Splicing |
| المشرفين على المادة: | 0 (RNA Splice Sites) |
| تواريخ الأحداث: | Date Created: 20240404 Date Completed: 20240504 Latest Revision: 20240504 |
| رمز التحديث: | 20240505 |
| DOI: | 10.1016/j.gene.2024.148429 |
| PMID: | 38575098 |
| قاعدة البيانات: | MEDLINE |
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