Task Balanced Multimodal Feature Selection to Predict the Progression of Alzheimer's Disease
Lodewijk Brand, Braedon O'Callaghan, Anthony Sun, Hua Wang
BIBE - 2020
The social and financial costs associated with Alzheimer’s disease (AD) result in significant burdens on our society. In order to understand the causes of this disease, public-private partnerships such as the Alzheimer’s Disease Neuroimaging Initiative (ADNI) release data into the scientific community. These data are organized into various modalities (genetic, brain-imaging, cognitive scores, diagnoses, etc.) for analysis. Many statistical learning approaches used in medical image analysis do not explicitly take advantage of this multimodal data structure. In this work we propose a novel objective function and optimization algorithm that is designed to handle multimodal information for the prediction and analysis of AD. Our approach relies on robust matrix-factorization and row-wise sparsity provided by the l21-norm in order to integrate multimodal data provided by the ADNI. These techniques are jointly optimized with a classification task to guide the feature selection in our proposed Task Balanced Multimodal Feature Selection method. Our results, when compared against some widely used machine learning algorithms, show improved balanced accuracies, precision, and Matthew’s correlation coefficients for identifying cognitive decline. In addition to the improved prediction performance, our method is able to identify brain and genetic biomarkers that are of interest to the clinical research community. Our experiments validate existing brain biomarkers and single nucleotide polymorphisms located on chromosome 11 and detail novel polymorphisms on chromosome 10 that, to the best of the authors’ knowledge, have not previously been reported. We anticipate that our method will be of interest to the greater research community and have released our method’s code online.
Links
- View publications from Anthony Sun
- View publications from Braedon O'Callaghan
- View publications from Lodewijk Brand
- View publications from Hua Wang
- View publications presented in BIBE
- View publications in the project, An Intelligence-Driven Patient Care Approach to Reduce Medical Errors
- View publications in the project, Intelligent Prediction of Traffic Conditions via Integrated Data-Driven Crowdsourcing and Learning
- View publications in the project, Mining Brain Imaging Genomics Data for Improved Cognitive Health
- View publications in the project, Prediction of coronavirus infections and complications at the individual and the population levels from genomic, proteomic, clinical and behavioral data sources
- View publications researching Embeddings
- View publications researching Multi-Modal/View Data Fusion
- View publications researching Robust Learning Models
- View publications researching Sparsity / Sparse Coding
- View publications applied to Bioinformatics
- View publications applied to Cheminformatics
- View publications applied to Medical Image Computing
Cite this paper
MLA
Brand, Lodewijk, et al. "Task Balanced Multimodal Feature Selection to Predict the Progression of Alzheimer’s Disease." 2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE). IEEE, 2020.
BibTeX
@inproceedings{brand2020task,
title={Task Balanced Multimodal Feature Selection to Predict the Progression of Alzheimer’s Disease},
author={Brand, Lodewijk and O’Callaghan, Braedon and Sun, Anthony and Wang, Hua},
booktitle={2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE)},
pages={196--203},
year={2020},
organization={IEEE}
}