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Unified mRNA Subcellular Localization Predictor based on machine learning techniques.
- Source :
-
BMC Genomics . 2/7/2024, Vol. 25 Issue 1, p1-18. 18p. - Publication Year :
- 2024
-
Abstract
- Background: The mRNA subcellular localization bears substantial impact in the regulation of gene expression, cellular migration, and adaptation. However, the methods employed for experimental determination of this localization are arduous, time-intensive, and come with a high cost. Methods: In this research article, we tackle the essential challenge of predicting the subcellular location of messenger RNAs (mRNAs) through Unified mRNA Subcellular Localization Predictor (UMSLP), a machine learning (ML) based approach. We embrace an in silico strategy that incorporate four distinct feature sets: kmer, pseudo k-tuple nucleotide composition, nucleotide physicochemical attributes, and the 3D sequence depiction achieved via Z-curve transformation for predicting subcellular localization in benchmark dataset across five distinct subcellular locales, encompassing nucleus, cytoplasm, extracellular region (ExR), mitochondria, and endoplasmic reticulum (ER). Results: The proposed ML model UMSLP attains cutting-edge outcomes in predicting mRNA subcellular localization. On independent testing dataset, UMSLP ahcieved over 87% precision, 94% specificity, and 94% accuracy. Compared to other existing tools, UMSLP outperformed mRNALocator, mRNALoc, and SubLocEP by 11%, 21%, and 32%, respectively on average prediction accuracy for all five locales. SHapley Additive exPlanations analysis highlights the dominance of k-mer features in predicting cytoplasm, nucleus, ER, and ExR localizations, while Z-curve based features play pivotal roles in mitochondria subcellular localization detection. Availability: We have shared datasets, code, Docker API for users in GitHub at: https://github.com/smusleh/UMSLP. [ABSTRACT FROM AUTHOR]
- Subjects :
- *MACHINE learning
*MESSENGER RNA
*GENETIC regulation
*ENDOPLASMIC reticulum
Subjects
Details
- Language :
- English
- ISSN :
- 14712164
- Volume :
- 25
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- BMC Genomics
- Publication Type :
- Academic Journal
- Accession number :
- 175304675
- Full Text :
- https://doi.org/10.1186/s12864-024-10077-9