Predicting Functional miRNA Targets in C-Fos with High Accuracy via Machine Learning

سال انتشار: 1402
محل انتشار: دوازدهمین همایش ملی و سومین همایش بین المللی بیوانفورماتیک
کد COI اختصاصی: IBIS12_066
زبان مقاله: انگلیسی
تعداد مشاهده: 173

نویسندگان

Department of Molecular Genetics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran

Farzanegan campus, semnan University, Semnan, Iran

Endocrinology and Metabolism Research Institute (EMRI), Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran

Majid Sadeghizadeh

Department of Molecular Genetics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran

چکیده

Introduction: The proto-oncogene C-Fos plays a crucial role in cell proliferation anddifferentiation[۱], yet its intricate control network, particularly involving microRNAs (miRNAs), remains largelyuncharted. This study delves into the possibility of C-Fos harboring encoded miRNA regulatory information,employing machine learning to decipher its miRNA regulatory landscape.Materials and Methods: We compiled diverse datasets encompassing C-Fos expression, miRNA expression, andpredicted miRNA target sites within the C-Fos gene. Utilizing feature engineering techniques, we extractedsequence and positional features surrounding potential target sites. Subsequently, we implemented variousmachine learning algorithms, including random forests and support vector machines, to classify true miRNA targetsites based on these features. We evaluated model performance using cross-validation and compared results acrossalgorithms.Results: Our models achieved high accuracy in identifying true miRNA target sites within the C-Fos gene,exceeding ۸۰% in most cases. By analyzing the learned feature weights, we uncovered key sequence andpositional motifs crucial for miRNA recognition and binding. Interestingly, we identified clusters of enrichedmotifs within the C-Fos gene, suggesting the presence of miRNA regulatory hotspots. Furthermore, we mappedthese hotspots to specific functional domains within C-Fos, revealing a potential link between miRNA regulationand C-Fos function.Conclusions: Our study demonstrates the effectiveness of machine learning in decoding miRNA regulatorynetworks encoded within genes. By applying this approach to C-Fos, we unraveled a complex miRNA regulatorylandscape with hotspots likely linked to specific C-Fos functions. This understanding paves the way for furtherinvestigations into miRNA-mediated control of C-Fos in diverse cellular processes, including cancerdevelopment[۲]. Future research will focus on experimentally validating predicted target sites and elucidating thefunctional consequences of this newly discovered miRNA regulatory network in various biological contexts.

کلیدواژه ها

C-Fos; microRNA; miRNA regulatory network; machine learning; target site prediction; cellproliferation.

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