Revolutionizing colorectal cancer detection: A breakthrough in microbiome data analysis.
The emergence of Next Generation Sequencing (NGS) technology has catalyzed a paradigm shift in clinical diagnostics and personalized medicine, enabling unprecedented access to high-throughput microbiome data. However, the inherent high dimensionality, noise, and variability of microbiome data presen...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0316493 |
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| _version_ | 1825206776132272128 |
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| author | Mwenge Mulenga Arutchelvan Rajamanikam Suresh Kumar Saharuddin Bin Muhammad Subha Bhassu Chandramathi Samudid Aznul Qalid Md Sabri Manjeevan Seera Christopher Ifeanyi Eke |
| author_facet | Mwenge Mulenga Arutchelvan Rajamanikam Suresh Kumar Saharuddin Bin Muhammad Subha Bhassu Chandramathi Samudid Aznul Qalid Md Sabri Manjeevan Seera Christopher Ifeanyi Eke |
| author_sort | Mwenge Mulenga |
| collection | DOAJ |
| description | The emergence of Next Generation Sequencing (NGS) technology has catalyzed a paradigm shift in clinical diagnostics and personalized medicine, enabling unprecedented access to high-throughput microbiome data. However, the inherent high dimensionality, noise, and variability of microbiome data present substantial obstacles to conventional statistical methods and machine learning techniques. Even the promising deep learning (DL) methods are not immune to these challenges. This paper introduces a novel feature engineering method that circumvents these limitations by amalgamating two feature sets derived from input data to generate a new dataset, which is then subjected to feature selection. This innovative approach markedly enhances the Area Under the Curve (AUC) performance of the Deep Neural Network (DNN) algorithm in colorectal cancer (CRC) detection using gut microbiome data, elevating it from 0.800 to 0.923. The proposed method constitutes a significant advancement in the field, providing a robust solution to the intricacies of microbiome data analysis and amplifying the potential of DL methods in disease detection. |
| format | Article |
| id | doaj-art-2cfc25e764b44fce8838669faa387730 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-2cfc25e764b44fce8838669faa3877302025-02-07T05:30:50ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01201e031649310.1371/journal.pone.0316493Revolutionizing colorectal cancer detection: A breakthrough in microbiome data analysis.Mwenge MulengaArutchelvan RajamanikamSuresh KumarSaharuddin Bin MuhammadSubha BhassuChandramathi SamudidAznul Qalid Md SabriManjeevan SeeraChristopher Ifeanyi EkeThe emergence of Next Generation Sequencing (NGS) technology has catalyzed a paradigm shift in clinical diagnostics and personalized medicine, enabling unprecedented access to high-throughput microbiome data. However, the inherent high dimensionality, noise, and variability of microbiome data present substantial obstacles to conventional statistical methods and machine learning techniques. Even the promising deep learning (DL) methods are not immune to these challenges. This paper introduces a novel feature engineering method that circumvents these limitations by amalgamating two feature sets derived from input data to generate a new dataset, which is then subjected to feature selection. This innovative approach markedly enhances the Area Under the Curve (AUC) performance of the Deep Neural Network (DNN) algorithm in colorectal cancer (CRC) detection using gut microbiome data, elevating it from 0.800 to 0.923. The proposed method constitutes a significant advancement in the field, providing a robust solution to the intricacies of microbiome data analysis and amplifying the potential of DL methods in disease detection.https://doi.org/10.1371/journal.pone.0316493 |
| spellingShingle | Mwenge Mulenga Arutchelvan Rajamanikam Suresh Kumar Saharuddin Bin Muhammad Subha Bhassu Chandramathi Samudid Aznul Qalid Md Sabri Manjeevan Seera Christopher Ifeanyi Eke Revolutionizing colorectal cancer detection: A breakthrough in microbiome data analysis. PLoS ONE |
| title | Revolutionizing colorectal cancer detection: A breakthrough in microbiome data analysis. |
| title_full | Revolutionizing colorectal cancer detection: A breakthrough in microbiome data analysis. |
| title_fullStr | Revolutionizing colorectal cancer detection: A breakthrough in microbiome data analysis. |
| title_full_unstemmed | Revolutionizing colorectal cancer detection: A breakthrough in microbiome data analysis. |
| title_short | Revolutionizing colorectal cancer detection: A breakthrough in microbiome data analysis. |
| title_sort | revolutionizing colorectal cancer detection a breakthrough in microbiome data analysis |
| url | https://doi.org/10.1371/journal.pone.0316493 |
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