DrOGA: An Artificial Intelligence Solution for Driver-Status Prediction of Genomics Mutations in Precision Cancer Medicine
Precision cancer medicine suggests that better cancer treatments would be possible guiding therapies by tumor’s genomics alterations. This hypothesis boosted exome sequencing studies, collection of cancer variants databases and developing of statistical and Machine Learning-driven methods...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10101788/ |
| _version_ | 1797839198941609984 |
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| author | Matteo Bastico Anaida Fernandez-Garcia Alberto Belmonte-Hernandez Silvia Uribe Mayoral |
| author_facet | Matteo Bastico Anaida Fernandez-Garcia Alberto Belmonte-Hernandez Silvia Uribe Mayoral |
| author_sort | Matteo Bastico |
| collection | DOAJ |
| description | Precision cancer medicine suggests that better cancer treatments would be possible guiding therapies by tumor’s genomics alterations. This hypothesis boosted exome sequencing studies, collection of cancer variants databases and developing of statistical and Machine Learning-driven methods for alterations’ analysis. In order to extract relevant information from huge exome sequencing data, accurate methods to distinguish driver and neutral or passengers mutations are vital. Nevertheless, traditional variant classification methods have often low precision in favour of higher recall. Here, we propose several traditional Machine Learning and new Deep Learning techniques to finely classify driver somatic non-synonymous mutations based on a 70-features annotation, derived from medical and statistical tools. We collected and annotated a complete database containing driver and neutral alterations from various public data sources. Our framework, called Driver-Oriented Genomics Analysis (DrOGA), presents the best performances compared to individual and other ensemble methods on our data. Explainable Artificial Intelligence is used to provide visual and clinical explanation of the results, with a particular focus on the most relevant annotations. This analysis and the proposed tool, along with the collected database and the feature engineering pipeline suggested, can help the study of genomics alterations in human cancers allowing precision oncology targeted therapies based on personal data from next-generation sequencing. |
| first_indexed | 2024-04-09T15:54:13Z |
| format | Article |
| id | doaj.art-90f6f6ff18294c94a7ea585d4c85eb3b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-09T15:54:13Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-90f6f6ff18294c94a7ea585d4c85eb3b2023-04-25T23:00:22ZengIEEEIEEE Access2169-35362023-01-0111373783739110.1109/ACCESS.2023.326698310101788DrOGA: An Artificial Intelligence Solution for Driver-Status Prediction of Genomics Mutations in Precision Cancer MedicineMatteo Bastico0https://orcid.org/0000-0003-4125-6304Anaida Fernandez-Garcia1https://orcid.org/0000-0002-6102-3121Alberto Belmonte-Hernandez2https://orcid.org/0000-0002-4009-2662Silvia Uribe Mayoral3https://orcid.org/0000-0001-6156-5492Técnica Superior de Ingenieros de Telecomunicacións, Universidad Politécnica de Madrid, Madrid, SpainTécnica Superior de Ingenieros de Telecomunicacións, Universidad Politécnica de Madrid, Madrid, SpainTécnica Superior de Ingenieros de Telecomunicacións, Universidad Politécnica de Madrid, Madrid, SpainTécnica Superior de Ingeniería de Sistemas Informáticos (ETSISI), Universidad Politécnica, Madrid, SpainPrecision cancer medicine suggests that better cancer treatments would be possible guiding therapies by tumor’s genomics alterations. This hypothesis boosted exome sequencing studies, collection of cancer variants databases and developing of statistical and Machine Learning-driven methods for alterations’ analysis. In order to extract relevant information from huge exome sequencing data, accurate methods to distinguish driver and neutral or passengers mutations are vital. Nevertheless, traditional variant classification methods have often low precision in favour of higher recall. Here, we propose several traditional Machine Learning and new Deep Learning techniques to finely classify driver somatic non-synonymous mutations based on a 70-features annotation, derived from medical and statistical tools. We collected and annotated a complete database containing driver and neutral alterations from various public data sources. Our framework, called Driver-Oriented Genomics Analysis (DrOGA), presents the best performances compared to individual and other ensemble methods on our data. Explainable Artificial Intelligence is used to provide visual and clinical explanation of the results, with a particular focus on the most relevant annotations. This analysis and the proposed tool, along with the collected database and the feature engineering pipeline suggested, can help the study of genomics alterations in human cancers allowing precision oncology targeted therapies based on personal data from next-generation sequencing.https://ieeexplore.ieee.org/document/10101788/Genomicsmutationartificial intelligencemachine learningdeep learningexplainable AI |
| spellingShingle | Matteo Bastico Anaida Fernandez-Garcia Alberto Belmonte-Hernandez Silvia Uribe Mayoral DrOGA: An Artificial Intelligence Solution for Driver-Status Prediction of Genomics Mutations in Precision Cancer Medicine IEEE Access Genomics mutation artificial intelligence machine learning deep learning explainable AI |
| title | DrOGA: An Artificial Intelligence Solution for Driver-Status Prediction of Genomics Mutations in Precision Cancer Medicine |
| title_full | DrOGA: An Artificial Intelligence Solution for Driver-Status Prediction of Genomics Mutations in Precision Cancer Medicine |
| title_fullStr | DrOGA: An Artificial Intelligence Solution for Driver-Status Prediction of Genomics Mutations in Precision Cancer Medicine |
| title_full_unstemmed | DrOGA: An Artificial Intelligence Solution for Driver-Status Prediction of Genomics Mutations in Precision Cancer Medicine |
| title_short | DrOGA: An Artificial Intelligence Solution for Driver-Status Prediction of Genomics Mutations in Precision Cancer Medicine |
| title_sort | droga an artificial intelligence solution for driver status prediction of genomics mutations in precision cancer medicine |
| topic | Genomics mutation artificial intelligence machine learning deep learning explainable AI |
| url | https://ieeexplore.ieee.org/document/10101788/ |
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