Integrative analyses in omics data: Machine learning perspective
Developments in the high throughput technologies have enabled the production of an immense amount of knowledge at the multi-omics level. Considering complex diseases which are affected by multi-factors, single omics datasets might not be sufficient to unveil the molecular mechanisms of heterogeneous...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | deu |
| Published: |
German Medical Science GMS Publishing House
2023-07-01
|
| Series: | GMS Medizinische Informatik, Biometrie und Epidemiologie |
| Online Access: | http://www.egms.de/static/en/journals/mibe/2023-19/mibe000244.shtml |
| _version_ | 1797770579003047936 |
|---|---|
| author | Unlu Yazici, Miray Bakir-Gungor, Burcu Yousef, Malik |
| author_facet | Unlu Yazici, Miray Bakir-Gungor, Burcu Yousef, Malik |
| author_sort | Unlu Yazici, Miray |
| collection | DOAJ |
| description | Developments in the high throughput technologies have enabled the production of an immense amount of knowledge at the multi-omics level. Considering complex diseases which are affected by multi-factors, single omics datasets might not be sufficient to unveil the molecular mechanisms of heterogeneous diseases. Providing a comprehensive and systematic overview to explain disease hallmarks in significant depth is critical. Utilizing multi-omics datasets has led to the development of a variety of tools and platforms. Machine learning models are utilized in a wide variety of tools to tackle the complexity of disorders and to identify new biomolecular signatures and potential markers. Underlying aspects of these approaches are based on training the models for making predictions and classification of the given data. In this review, we describe current machine learning-based approaches and available implementations. Challenges in the enlightenment of disease mechanisms of onset and progression and future development of the field of medicine will be discussed. The prominence of biological interpretation of model output with corresponding biological knowledge will be also covered in this review. |
| first_indexed | 2024-03-12T21:25:12Z |
| format | Article |
| id | doaj.art-f865bf729d054293987e94f1469aa12d |
| institution | Directory Open Access Journal |
| issn | 1860-9171 |
| language | deu |
| last_indexed | 2024-03-12T21:25:12Z |
| publishDate | 2023-07-01 |
| publisher | German Medical Science GMS Publishing House |
| record_format | Article |
| series | GMS Medizinische Informatik, Biometrie und Epidemiologie |
| spelling | doaj.art-f865bf729d054293987e94f1469aa12d2023-07-28T10:01:40ZdeuGerman Medical Science GMS Publishing HouseGMS Medizinische Informatik, Biometrie und Epidemiologie1860-91712023-07-0119Doc0510.3205/mibe000244Integrative analyses in omics data: Machine learning perspectiveUnlu Yazici, Miray0Bakir-Gungor, Burcu1Yousef, Malik2Department of Bioengineering, Faculty of Engineering, Abdullah Gül University, Kayseri, TurkeyDepartment of Computer Engineering, Faculty of Engineering, Abdullah Gül University, Kayseri, TurkeyDepartment of Information Systems, Zefat Academic College, Zefat, IsraelDevelopments in the high throughput technologies have enabled the production of an immense amount of knowledge at the multi-omics level. Considering complex diseases which are affected by multi-factors, single omics datasets might not be sufficient to unveil the molecular mechanisms of heterogeneous diseases. Providing a comprehensive and systematic overview to explain disease hallmarks in significant depth is critical. Utilizing multi-omics datasets has led to the development of a variety of tools and platforms. Machine learning models are utilized in a wide variety of tools to tackle the complexity of disorders and to identify new biomolecular signatures and potential markers. Underlying aspects of these approaches are based on training the models for making predictions and classification of the given data. In this review, we describe current machine learning-based approaches and available implementations. Challenges in the enlightenment of disease mechanisms of onset and progression and future development of the field of medicine will be discussed. The prominence of biological interpretation of model output with corresponding biological knowledge will be also covered in this review.http://www.egms.de/static/en/journals/mibe/2023-19/mibe000244.shtml |
| spellingShingle | Unlu Yazici, Miray Bakir-Gungor, Burcu Yousef, Malik Integrative analyses in omics data: Machine learning perspective GMS Medizinische Informatik, Biometrie und Epidemiologie |
| title | Integrative analyses in omics data: Machine learning perspective |
| title_full | Integrative analyses in omics data: Machine learning perspective |
| title_fullStr | Integrative analyses in omics data: Machine learning perspective |
| title_full_unstemmed | Integrative analyses in omics data: Machine learning perspective |
| title_short | Integrative analyses in omics data: Machine learning perspective |
| title_sort | integrative analyses in omics data machine learning perspective |
| url | http://www.egms.de/static/en/journals/mibe/2023-19/mibe000244.shtml |
| work_keys_str_mv | AT unluyazicimiray integrativeanalysesinomicsdatamachinelearningperspective AT bakirgungorburcu integrativeanalysesinomicsdatamachinelearningperspective AT yousefmalik integrativeanalysesinomicsdatamachinelearningperspective |