Machine Learning-Based Ensemble Feature Selection and Nested Cross-Validation for miRNA Biomarker Discovery in Usher Syndrome
Usher syndrome (USH) is a rare genetic disorder affecting vision, hearing, and balance. Identifying reliable biomarkers is crucial for early diagnosis and understanding disease mechanisms. MicroRNAs (miRNAs), key regulators of gene expression, hold promise as biomarkers for USH. This study aimed to...
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| Format: | Article |
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2025-05-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/12/5/497 |
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| author | Rama Krishna Thelagathoti Dinesh S. Chandel Wesley A. Tom Chao Jiang Gary Krzyzanowski Appolinaire Olou M. Rohan Fernando |
| author_facet | Rama Krishna Thelagathoti Dinesh S. Chandel Wesley A. Tom Chao Jiang Gary Krzyzanowski Appolinaire Olou M. Rohan Fernando |
| author_sort | Rama Krishna Thelagathoti |
| collection | DOAJ |
| description | Usher syndrome (USH) is a rare genetic disorder affecting vision, hearing, and balance. Identifying reliable biomarkers is crucial for early diagnosis and understanding disease mechanisms. MicroRNAs (miRNAs), key regulators of gene expression, hold promise as biomarkers for USH. This study aimed to identify a minimal subset of miRNAs that could serve as biomarkers to effectively differentiate USH from controls. We employed ensemble feature selection techniques to select the top miRNAs appearing in at least three algorithms. Machine learning models were trained and tested using this subset, followed by validation on an independent 10% sample. Our approach identified 10 key miRNAs as potential biomarkers for USH. To further validate their biological relevance, we conducted pathway analysis, which revealed significant pathways associated with USH. Furthermore, our approach achieved high classification performance, with an accuracy of 97.7%, sensitivity of 98%, specificity of 92.5%, F1 score of 95.8%, and an AUC of 97.5%. These findings demonstrate that combining ensemble feature selection with machine learning provides a robust strategy for miRNA biomarker discovery, advancing USH diagnosis and molecular understanding. |
| format | Article |
| id | doaj-art-36c25a879cdf4ba1981dec9fcdc6d2bc |
| institution | OA Journals |
| issn | 2306-5354 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj-art-36c25a879cdf4ba1981dec9fcdc6d2bc2025-08-20T01:56:17ZengMDPI AGBioengineering2306-53542025-05-0112549710.3390/bioengineering12050497Machine Learning-Based Ensemble Feature Selection and Nested Cross-Validation for miRNA Biomarker Discovery in Usher SyndromeRama Krishna Thelagathoti0Dinesh S. Chandel1Wesley A. Tom2Chao Jiang3Gary Krzyzanowski4Appolinaire Olou5M. Rohan Fernando6Molecular Diagnostic Research Laboratory, Center for Sensory Neuroscience, Boys Town National Research Hospital, Omaha, NE 68010, USAMolecular Diagnostic Research Laboratory, Center for Sensory Neuroscience, Boys Town National Research Hospital, Omaha, NE 68010, USAMolecular Diagnostic Research Laboratory, Center for Sensory Neuroscience, Boys Town National Research Hospital, Omaha, NE 68010, USAMolecular Diagnostic Research Laboratory, Center for Sensory Neuroscience, Boys Town National Research Hospital, Omaha, NE 68010, USAMolecular Diagnostic Research Laboratory, Center for Sensory Neuroscience, Boys Town National Research Hospital, Omaha, NE 68010, USAMolecular Diagnostic Research Laboratory, Center for Sensory Neuroscience, Boys Town National Research Hospital, Omaha, NE 68010, USAMolecular Diagnostic Research Laboratory, Center for Sensory Neuroscience, Boys Town National Research Hospital, Omaha, NE 68010, USAUsher syndrome (USH) is a rare genetic disorder affecting vision, hearing, and balance. Identifying reliable biomarkers is crucial for early diagnosis and understanding disease mechanisms. MicroRNAs (miRNAs), key regulators of gene expression, hold promise as biomarkers for USH. This study aimed to identify a minimal subset of miRNAs that could serve as biomarkers to effectively differentiate USH from controls. We employed ensemble feature selection techniques to select the top miRNAs appearing in at least three algorithms. Machine learning models were trained and tested using this subset, followed by validation on an independent 10% sample. Our approach identified 10 key miRNAs as potential biomarkers for USH. To further validate their biological relevance, we conducted pathway analysis, which revealed significant pathways associated with USH. Furthermore, our approach achieved high classification performance, with an accuracy of 97.7%, sensitivity of 98%, specificity of 92.5%, F1 score of 95.8%, and an AUC of 97.5%. These findings demonstrate that combining ensemble feature selection with machine learning provides a robust strategy for miRNA biomarker discovery, advancing USH diagnosis and molecular understanding.https://www.mdpi.com/2306-5354/12/5/497ensemble feature selectionbiomarker discoveryusher syndromemiRNAmachine learningnested cross-validation |
| spellingShingle | Rama Krishna Thelagathoti Dinesh S. Chandel Wesley A. Tom Chao Jiang Gary Krzyzanowski Appolinaire Olou M. Rohan Fernando Machine Learning-Based Ensemble Feature Selection and Nested Cross-Validation for miRNA Biomarker Discovery in Usher Syndrome Bioengineering ensemble feature selection biomarker discovery usher syndrome miRNA machine learning nested cross-validation |
| title | Machine Learning-Based Ensemble Feature Selection and Nested Cross-Validation for miRNA Biomarker Discovery in Usher Syndrome |
| title_full | Machine Learning-Based Ensemble Feature Selection and Nested Cross-Validation for miRNA Biomarker Discovery in Usher Syndrome |
| title_fullStr | Machine Learning-Based Ensemble Feature Selection and Nested Cross-Validation for miRNA Biomarker Discovery in Usher Syndrome |
| title_full_unstemmed | Machine Learning-Based Ensemble Feature Selection and Nested Cross-Validation for miRNA Biomarker Discovery in Usher Syndrome |
| title_short | Machine Learning-Based Ensemble Feature Selection and Nested Cross-Validation for miRNA Biomarker Discovery in Usher Syndrome |
| title_sort | machine learning based ensemble feature selection and nested cross validation for mirna biomarker discovery in usher syndrome |
| topic | ensemble feature selection biomarker discovery usher syndrome miRNA machine learning nested cross-validation |
| url | https://www.mdpi.com/2306-5354/12/5/497 |
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