Multiple-kernel learning for genomic data mining and prediction
Abstract Background Advances in medical technology have allowed for customized prognosis, diagnosis, and treatment regimens that utilize multiple heterogeneous data sources. Multiple kernel learning (MKL) is well suited for the integration of multiple high throughput data sources. MKL remains to be...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2019-08-01
|
| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12859-019-2992-1 |
| _version_ | 1818882719802720256 |
|---|---|
| author | Christopher M. Wilson Kaiqiao Li Xiaoqing Yu Pei-Fen Kuan Xuefeng Wang |
| author_facet | Christopher M. Wilson Kaiqiao Li Xiaoqing Yu Pei-Fen Kuan Xuefeng Wang |
| author_sort | Christopher M. Wilson |
| collection | DOAJ |
| description | Abstract Background Advances in medical technology have allowed for customized prognosis, diagnosis, and treatment regimens that utilize multiple heterogeneous data sources. Multiple kernel learning (MKL) is well suited for the integration of multiple high throughput data sources. MKL remains to be under-utilized by genomic researchers partly due to the lack of unified guidelines for its use, and benchmark genomic datasets. Results We provide three implementations of MKL in R. These methods are applied to simulated data to illustrate that MKL can select appropriate models. We also apply MKL to combine clinical information with miRNA gene expression data of ovarian cancer study into a single analysis. Lastly, we show that MKL can identify gene sets that are known to play a role in the prognostic prediction of 15 cancer types using gene expression data from The Cancer Genome Atlas, as well as, identify new gene sets for the future research. Conclusion Multiple kernel learning coupled with modern optimization techniques provides a promising learning tool for building predictive models based on multi-source genomic data. MKL also provides an automated scheme for kernel prioritization and parameter tuning. The methods used in the paper are implemented as an R package called RMKL package, which is freely available for download through CRAN at https://CRAN.R-project.org/package=RMKL. |
| first_indexed | 2024-12-19T15:22:14Z |
| format | Article |
| id | doaj.art-34f2d3acce89480fa1aaa76c0bab2e70 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-19T15:22:14Z |
| publishDate | 2019-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-34f2d3acce89480fa1aaa76c0bab2e702022-12-21T20:15:58ZengBMCBMC Bioinformatics1471-21052019-08-012011710.1186/s12859-019-2992-1Multiple-kernel learning for genomic data mining and predictionChristopher M. Wilson0Kaiqiao Li1Xiaoqing Yu2Pei-Fen Kuan3Xuefeng Wang4Department of Biostatistics and Bioinformatics at Moffitt Cancer CenterDepartment of Applied Mathematics and Statistics at Stony Brook UniversityDepartment of Biostatistics and Bioinformatics at Moffitt Cancer CenterDepartment of Applied Mathematics and Statistics at Stony Brook UniversityDepartment of Biostatistics and Bioinformatics at Moffitt Cancer CenterAbstract Background Advances in medical technology have allowed for customized prognosis, diagnosis, and treatment regimens that utilize multiple heterogeneous data sources. Multiple kernel learning (MKL) is well suited for the integration of multiple high throughput data sources. MKL remains to be under-utilized by genomic researchers partly due to the lack of unified guidelines for its use, and benchmark genomic datasets. Results We provide three implementations of MKL in R. These methods are applied to simulated data to illustrate that MKL can select appropriate models. We also apply MKL to combine clinical information with miRNA gene expression data of ovarian cancer study into a single analysis. Lastly, we show that MKL can identify gene sets that are known to play a role in the prognostic prediction of 15 cancer types using gene expression data from The Cancer Genome Atlas, as well as, identify new gene sets for the future research. Conclusion Multiple kernel learning coupled with modern optimization techniques provides a promising learning tool for building predictive models based on multi-source genomic data. MKL also provides an automated scheme for kernel prioritization and parameter tuning. The methods used in the paper are implemented as an R package called RMKL package, which is freely available for download through CRAN at https://CRAN.R-project.org/package=RMKL.http://link.springer.com/article/10.1186/s12859-019-2992-1ClassificationMultiple kernel learningGenomicsData integrationMachine learningKernel methods |
| spellingShingle | Christopher M. Wilson Kaiqiao Li Xiaoqing Yu Pei-Fen Kuan Xuefeng Wang Multiple-kernel learning for genomic data mining and prediction BMC Bioinformatics Classification Multiple kernel learning Genomics Data integration Machine learning Kernel methods |
| title | Multiple-kernel learning for genomic data mining and prediction |
| title_full | Multiple-kernel learning for genomic data mining and prediction |
| title_fullStr | Multiple-kernel learning for genomic data mining and prediction |
| title_full_unstemmed | Multiple-kernel learning for genomic data mining and prediction |
| title_short | Multiple-kernel learning for genomic data mining and prediction |
| title_sort | multiple kernel learning for genomic data mining and prediction |
| topic | Classification Multiple kernel learning Genomics Data integration Machine learning Kernel methods |
| url | http://link.springer.com/article/10.1186/s12859-019-2992-1 |
| work_keys_str_mv | AT christophermwilson multiplekernellearningforgenomicdataminingandprediction AT kaiqiaoli multiplekernellearningforgenomicdataminingandprediction AT xiaoqingyu multiplekernellearningforgenomicdataminingandprediction AT peifenkuan multiplekernellearningforgenomicdataminingandprediction AT xuefengwang multiplekernellearningforgenomicdataminingandprediction |