Stratifying patients using fast multiple kernel learning framework: case studies of Alzheimer’s disease and cancers
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BACKGROUND: Predictive patient stratification is greatly emerging, because it allows us to prospectively identify which patients will benefit from what interventions before their condition worsens. In the biomedical research, a number of stratification methods have been successfully applied and have assisted treatment process. Because of heterogeneity and complexity of medical data, it is very challenging to integrate them and make use of them in practical clinic. There are two major challenges of data integration. Firstly, since the biomedical data has a high number of dimensions, combining multiple data leads to the hard problem of vast dimensional space handling. The computation is enormously complex and time-consuming. Secondly, the disparity of different data types causes another critical problem in machine learning for biomedical data. It has a great need to develop an efficient machine learning framework to handle the challenges. METHODS: In this paper, we propose a fast-multiple kernel learning framework, referred to as fMKL-DR, that optimise equations to calculate matrix chain multiplication and reduce dimensions in data space. We applied our framework to two case studies, Alzheimer's disease (AD) patient stratification and cancer patient stratification. We performed several comparative evaluations on various biomedical datasets. RESULTS: In the case study of AD patients, we enhanced significantly the multiple-ROIs approach based on MRI image data. The method could successfully classify not only AD patients and non-AD patients but also different phases of AD patients with AUC close to 1. In the case study of cancer patients, the framework was applied to six types of cancers, i.e., glioblastoma multiforme cancer, ovarian cancer, lung cancer, breast cancer, kidney cancer, and liver cancer. We efficiently integrated gene expression, miRNA expression, and DNA methylation. The results showed that the classification model basing on integrated datasets was much more accurate than classification model basing on the single data type. CONCLUSIONS: The results demonstrated that the fMKL-DR remarkably improves computational cost and accuracy for both AD patient and cancer patient stratification. We optimised the data integration, dimension reduction, and kernel fusion. Our framework has great potential for mining large-scale cohort data and aiding personalised prevention.
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Multi-kernel learning techniques have improved integration of high-dimensional, multimodal datasets, enhancing performance and clinical applicability.
“Multi-kernel learning techniques have improved integration of high-dimensional, multimodal datasets, further enhancing performance and clinical applicability403.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [403]
fMKL-DR has been used with SVM for combining gene expression, miRNA expression, and DNA methylation data.
“Moreover, fMKL-DR (fast multiple kernel learning for dimensionality reduction) (Giang et al., 2020) has been used along with SVM for combining gene expression, miRNA expression, and DNA methylation data.”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Giang et al., 2020
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Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., & Joliot, M. (2002) · NeuroImage · doi:10.1006/nimg.2001.0978 · Cited by 16,319 · see citing works
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
Yan, S., Xu, D., Zhang, B., Zhang, H.-J., Yang, Q., & Lin, S. C.-F. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.250598 · Cited by 3,035 · see citing works
Fei-Fei, L., Fergus, R., & Perona, P. (2005) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvpr.2004.383 · Cited by 2,513 · see citing works
Similarity network fusion for aggregating data types on a genomic scale
Wang, B., Mezlini, A. M., Demir, F., Fiume, M., Tu, Z., Brudno, M., Haibe-Kains, B., & Goldenberg, A. (2014) · Nature Methods · doi:10.1038/nmeth.2810 · Cited by 2,036 · see citing works
Multiple kernel learning, conic duality, and the SMO algorithm
Bach, F. R., Lanckriet, G. R. G., & Jordan, M. I. (2004) · Twenty-first international conference on Machine learning - ICML '04 · doi:10.1145/1015330.1015424 · Cited by 1,453 · see citing works
Proceedings of the twenty-first international conference on Machine learning
Brodley, C. E. (2004) · Cited by 999 · see citing works
Hierarchical Feature Representation and Multimodal Fusion with Deep Learning for AD/MCI Diagnosis
Suk, H.-I., Lee, S.-W., & Shen, D. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.06.077 · Cited by 968 · see citing works
THE STATISTICAL UTILIZATION OF MULTIPLE MEASUREMENTS
Fisher, R. A. (1938) · Annals of Eugenics · doi:10.1111/j.1469-1809.1938.tb02189.x · Cited by 855 · see citing works
Marie Chupin, A., Gérardin, E., Cuingnet, R., Boutet, C., Lemieux, L., Lehéricy, S., Benali, H., Garnero, L., & Colliot, O. (2009) · Hippocampus · doi:10.1002/hipo.20626 · Cited by 341 · see citing works
Using Electronic Patient Records to Discover Disease Correlations and Stratify Patient Cohorts
Roque, F. S., Jensen, P. B., Schmock, H., Dalgaard, M., Andreatta, M., Hansen, T. F., Søeby, K., Bredkjær, S., Juul, A., Werge, T. M., et al. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002141 · Cited by 283 · see citing works
Localized multiple kernel learning
Gönen, M., & Alpaydın, E. (2008) · International Conference on Machine Learning · doi:10.1145/1390156.1390201 · Cited by 277 · see citing works
Khedher, L., Ramírez, J., Górriz, J. M., Brahim, A., & Segovia, F. (2015) · Neurocomputing · doi:10.1016/j.neucom.2014.09.072 · Cited by 262 · see citing works
Multiple Kernel Learning for Dimensionality Reduction
Lin, Y., Liu, T.-L., & Fuh, C. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.183 · Cited by 245 · see citing works
Integrative data analysis of multi-platform cancer data with a multimodal deep learning approach
Liang, M., Li, Z., Chen, T., & Zeng, J. (2015) · IEEE/ACM Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2014.2377729 · Cited by 231 · see citing works
Classification of Alzheimer's Disease Using Whole Brain Hierarchical Network
Liu, J., Li, M., Lan, W., Wu, F., Pan, Y. F., & Wang, J. (2016) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2016.2635144 · Cited by 212 · see citing works
Speicher, N. K., & Pfeifer, N. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv244 · Cited by 183 · see citing works
Genome-wide DNA methylation profiles in progression to
Fleischer, T., Frigessi, A., Johnson, K. C., Edvardsen, H., Touleimat, N., Klajic, J., Riis, M. L. H., Haakensen, V. D., Wärnberg, F., Naume, B., et al. (2014) · Genome biology · doi:10.1186/s13059-014-0435-x · Cited by 175 · see citing works
Classification of Alzheimer’s disease subjects from MRI using hippocampal visual features
Initiative, A. D. N., Ahmed, O. B., Benois‐Pineau, J., Michèle Allard, A., Amar, C. B., & Catheline, G. (2014) · Multimedia Tools and Applications · doi:10.1007/s11042-014-2123-y · Cited by 137 · see citing works
Biological heterogeneity in ADNI amnestic mild cognitive impairment
Nettiksimmons, J., DeCarli, C. S., Landau, S. M., Beckett, L. A., & Initiative, A. D. N. (2014) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2013.09.003 · Cited by 118 · see citing works
Valdés, G., Luna, J. M., Eaton, E., Simone, C. B., Ungar, L., & Solberg, T. D. (2016) · Scientific Reports · doi:10.1038/srep37854 · Cited by 118 · see citing works
Udali, S., Guarini, P., Ruzzenente, A., Ferrarini, A., Guglielmi, A., Lotto, V., Tononi, P., Pattini, P., Moruzzi, S., Campagnaro, T., et al. (2015) · Clinical Epigenetics · doi:10.1186/s13148-015-0077-1 · Cited by 103 · see citing works
Genome-Wide Analysis of DNA Methylation and the Gene Expression Change in Lung Cancer
Kwon, Y.-J., Lee, S. J., Koh, J. S., Kim, S., Lee, H. W., Kang, M. C., Bae, J. B., Kim, Y.-J., & Park, J. H. (2011) · Journal of Thoracic Oncology · doi:10.1097/jto.0b013e3182307f62 · Cited by 93 · see citing works
Improving Alzheimer's Disease Classification by Combining Multiple Measures
Liu, J., Wang, J., Tang, Z., Hu, B., Wu, F., & Pan, Y. F. (2017) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2017.2731849 · Cited by 79 · see citing works
microRNAs and cancer: role in tumorigenesis, patient classification and therapy
Hernando, E. (2007) · Clinical & Translational Oncology · doi:10.1007/s12094-007-0029-0 · Cited by 71 · see citing works
Alderdice, M., Richman, S. D., Gollins, S., Stewart, P., Hurt, C. N., Adams, R., McCorry, A. M. B., Roddy, A. C., Vimalachandran, D., Isella, C., et al. (2018) · The Journal of Pathology · doi:10.1002/path.5051 · Cited by 65 · see citing works
Alzheimer Disease and Other Dementias
Mulsant, B. H., & Thornton, J. E. (1990) · Springer eBooks · doi:10.1007/978-1-4899-0894-0_16 · Cited by 59 · see citing works
A comparison of machine learning techniques for survival prediction in breast cancer
Vanneschi, L., Farinaccio, A., Mauri, G., et al. (2011) · BioData Mining · doi:10.1186/1756-0381-4-12 · Cited by 55 · see citing works
Liu, J., Li, M., Pan, Y. F., Wu, F., Chen, X., & Wang, J. (2017) · IEEE Transactions on NanoBioscience · doi:10.1109/tnb.2017.2751074 · Cited by 52 · see citing works
Accurate prediction of AD patients using cortical thickness networks
Dai, D., He, H., Vogelstein, J. T., & Hou, Z. (2012) · Machine Vision and Applications · doi:10.1007/s00138-012-0462-0 · Cited by 46 · see citing works
Zhong, X. Y., Yang, H., Zhao, S., Shyr, Y., & Li, B. (2015) · BMC Genomics · doi:10.1186/1471-2164-16-s7-s7 · Cited by 41 · see citing works
Doerr, M. J., Edelman, E. A., Gabitzsch, E. K., Eng, C., & Teng, K. (2014) · Journal of Personalized Medicine · doi:10.3390/jpm4020115 · Cited by 39 · see citing works
Linn, K. A., Gaonkar, B., Satterthwaite, T. D., Doshi, J. J., Davatzikos, C. A., & Shinohara, R. T. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.02.044 · Cited by 30 · see citing works
Stratifying cancer patients based on multiple kernel learning and dimensionality reduction
Giang, T. T., Nguyen, T. P., & Tran, D. H. (2017) · International Conference on Knowledge and Systems Engineering/Proceedings · doi:10.1109/kse.2017.8119443 · Cited by 5 · see citing works
Self-citationTHE NEW HYBRID METHOD FOR CLASSIFICATION OF PATIENTS BY GENE EXPRESSION PROFILING
Coşgun, E., & Karaağaoğlu, E. (2011) · Springer eBooks · doi:10.1007/978-1-4614-0116-2_20 · Cited by 2 · see citing works
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Reel, P. S., Reel, S., Pearson, E., Trucco, E., & Jefferson, E. (2021) · Biotechnology Advances · doi:10.1016/j.biotechadv.2021.107739 · Cited by 903 · see citing works
Application of Artificial Intelligence in Lung Cancer
Chiu, H.-Y., Chao, H.-S., & Chen, Y.-M. (2022) · Cancers · doi:10.3390/cancers14061370 · Cited by 138 · see citing works
Ren, F., Wei, J., Chen, Q., Hu, M., Yu, L., Mi, J., Zhou, X., Qin, D., Wu, J., & Wu, A. (2025) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2025.07.030 · Cited by 97 · see citing works
- Authors
- Giang, T.-T., Nguyen, T.-P., & Tran, D.-H.
- Affiliations
- Tay Bac University (VN)
- University of Luxembourg (LU)
- Hanoi National University of Education (VN)
- Corresponding author
- Nguyen, Thanh-Phuong
- Venue
- BMC Medical Informatics and Decision Making
- Published
- 2020-06-16
- Volume
- 20
- Issue
- 1
- Pages
- 108-108
- ISSN
- 1472-6947 (ISSN-L)
- PMC
- PMC7296686
- OpenAlex
- W3035016737
- Cited by
- 26
- References
- 34
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 6 · 2023: 6 · 2022: 6 · 2021: 3 · 2020: 1
- FWCI
- 1.30
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Medical Informatics and Decision Making (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Greater South Information System
- Funders
- Vietnam Ministry of Education and Training (. B2020-SPH-11)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Keywords
- patient stratification, multiple kernel learning, Alzheimer's disease, cancer classification, multimodal data integration, dimensionality reduction, gene expression, microRNA expression, DNA methylation, kernel fusion, personalized medicine
- MeSH terms
- Machine Learning, Alzheimer Disease, Humans, Magnetic Resonance Imaging, Neoplasms
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