Bio-primed machine learning to enhance discovery of relevant biomarkers
Abstract Precision medicine relies on identifying reliable biomarkers for gene dependencies to tailor individualized therapeutic strategies. The advent of high-throughput technologies presents unprecedented opportunities to explore molecular disease mechanisms but also challenges due to high dimensi...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | npj Precision Oncology |
| Online Access: | https://doi.org/10.1038/s41698-025-00825-9 |
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| _version_ | 1823863440512385024 |
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| author | David M. Henke Alexander Renwick Joseph R. Zoeller Jitendra K. Meena Nicholas J. Neill Elizabeth A. Bowling Kristen L. Meerbrey Thomas F. Westbrook Lukas M. Simon |
| author_facet | David M. Henke Alexander Renwick Joseph R. Zoeller Jitendra K. Meena Nicholas J. Neill Elizabeth A. Bowling Kristen L. Meerbrey Thomas F. Westbrook Lukas M. Simon |
| author_sort | David M. Henke |
| collection | DOAJ |
| description | Abstract Precision medicine relies on identifying reliable biomarkers for gene dependencies to tailor individualized therapeutic strategies. The advent of high-throughput technologies presents unprecedented opportunities to explore molecular disease mechanisms but also challenges due to high dimensionality and collinearity among features. Traditional statistical methods often fall short in this context, necessitating novel computational approaches that harness the full potential of big data in bioinformatics. Here, we introduce a novel machine learning approach extending the Least Absolute Shrinkage and Selection Operator (LASSO) regression framework to incorporate biological knowledge, such as protein-protein interaction databases, into the regularization process. This bio-primed approach prioritizes variables that are both statistically significant and biologically relevant. Applying our method to multiple dependency datasets, we identified biomarkers which traditional methods overlooked. Our biologically informed LASSO method effectively identifies relevant biomarkers from high-dimensional collinear data, bridging the gap between statistical rigor and biological insight. This method holds promise for advancing personalized medicine by uncovering novel therapeutic targets and understanding the complex interplay of genetic and molecular factors in disease. |
| format | Article |
| id | doaj-art-d436801f29e44bffab4adc679525956f |
| institution | Kabale University |
| issn | 2397-768X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Precision Oncology |
| spelling | doaj-art-d436801f29e44bffab4adc679525956f2025-02-09T12:09:28ZengNature Portfolionpj Precision Oncology2397-768X2025-02-019111010.1038/s41698-025-00825-9Bio-primed machine learning to enhance discovery of relevant biomarkersDavid M. Henke0Alexander Renwick1Joseph R. Zoeller2Jitendra K. Meena3Nicholas J. Neill4Elizabeth A. Bowling5Kristen L. Meerbrey6Thomas F. Westbrook7Lukas M. Simon8Molecular Virology & Microbiology, Baylor College of MedicineDepartment of Statistics, Rice UniversityMedical Scientist Training Program, Baylor College of MedicineVerna and Marrs McLean Department of Biochemistry and Molecular Pharmacology, Baylor College of MedicineVerna and Marrs McLean Department of Biochemistry and Molecular Pharmacology, Baylor College of MedicineVerna and Marrs McLean Department of Biochemistry and Molecular Pharmacology, Baylor College of MedicineVerna and Marrs McLean Department of Biochemistry and Molecular Pharmacology, Baylor College of MedicineVerna and Marrs McLean Department of Biochemistry and Molecular Pharmacology, Baylor College of MedicineVerna and Marrs McLean Department of Biochemistry and Molecular Pharmacology, Baylor College of MedicineAbstract Precision medicine relies on identifying reliable biomarkers for gene dependencies to tailor individualized therapeutic strategies. The advent of high-throughput technologies presents unprecedented opportunities to explore molecular disease mechanisms but also challenges due to high dimensionality and collinearity among features. Traditional statistical methods often fall short in this context, necessitating novel computational approaches that harness the full potential of big data in bioinformatics. Here, we introduce a novel machine learning approach extending the Least Absolute Shrinkage and Selection Operator (LASSO) regression framework to incorporate biological knowledge, such as protein-protein interaction databases, into the regularization process. This bio-primed approach prioritizes variables that are both statistically significant and biologically relevant. Applying our method to multiple dependency datasets, we identified biomarkers which traditional methods overlooked. Our biologically informed LASSO method effectively identifies relevant biomarkers from high-dimensional collinear data, bridging the gap between statistical rigor and biological insight. This method holds promise for advancing personalized medicine by uncovering novel therapeutic targets and understanding the complex interplay of genetic and molecular factors in disease.https://doi.org/10.1038/s41698-025-00825-9 |
| spellingShingle | David M. Henke Alexander Renwick Joseph R. Zoeller Jitendra K. Meena Nicholas J. Neill Elizabeth A. Bowling Kristen L. Meerbrey Thomas F. Westbrook Lukas M. Simon Bio-primed machine learning to enhance discovery of relevant biomarkers npj Precision Oncology |
| title | Bio-primed machine learning to enhance discovery of relevant biomarkers |
| title_full | Bio-primed machine learning to enhance discovery of relevant biomarkers |
| title_fullStr | Bio-primed machine learning to enhance discovery of relevant biomarkers |
| title_full_unstemmed | Bio-primed machine learning to enhance discovery of relevant biomarkers |
| title_short | Bio-primed machine learning to enhance discovery of relevant biomarkers |
| title_sort | bio primed machine learning to enhance discovery of relevant biomarkers |
| url | https://doi.org/10.1038/s41698-025-00825-9 |
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