Pankhuri Singhal
ABSTRACT
Alzheimer's Disease (AD) is a growing pandemic affecting over 50 million individuals worldwide. While individual molecular traits have been found to be associated with AD at the DNA, RNA, protein, and epigenetic level, the underlying genetic etiology of AD remains unknown. Integrating multiple omics datatypes simultaneously has the potential to reveal interactions within and between these molecular features. In order to identify disease driving mechanism, a standardized framework for integrating multiomics data is needed. Due to high variability in size, structure, and availability of high-throughput omics data, there is currently no gold standard for combining different data types together in a biologically meaningful way. Thus, we propose a pathway-centric, neural network-based framework to integrate multiomics AD data. In this knowledge-driven approach, we evaluate different gene ontologies to map data to the pathway level. Preliminary results show integrating multiple datatypes under this framework produces more robust AD pathway models compared to models from single data types alone.
- Das, S., Majumder, P. P., Chatterjee, R., Chatterjee, A., & Mukhopadhyay, I. (2018). A powerful method to integrate genotype and gene expression data for dissecting the genetic architecture of a disease. Genomics, (March), 1--8.Google Scholar
- Kang, M., Kim, D., Gao, J., et al. (2017). Integration of multi-omics data for integrative gene regulatory network inference. International Journal of Data Mining and Bioinformatics, 18(3), 223. Google ScholarDigital Library
- Iqbal, K., Liu, F., Gong, C. X., & Grundke-Iqbal, I. 2010. Tau in Alzheimer disease and related tauopathies. Current Alzheimer research, 7(8), 656--64.Google Scholar
- Kim, D., Li, R., Dudek, S. M., Frase, et al. (2014). Knowledge-driven genomic interactions: An application in ovarian cancer. BioData Mining, 7(1), 1--11.Google ScholarCross Ref
- Holzinger, E. R., Dudek, S. M., Frase, A. T., Pendergrass, S. A., & Ritchie, M. D. (2014). ATHENA: The analysis tool for heritable and environmental network associations. Bioinformatics, 30(5), 698--705.Google ScholarCross Ref
- Kim, D., Li, R., Lucas, A., Verma, et al. (2017). Using knowledge-driven genomic interactions for multi-omics data analysis. Journal of the American Medical Informatics Association, 24(3), 577--587.Google ScholarCross Ref
- Neural network-based multiomics data integration in Alzheimer's disease
Recommendations
IPMM: Cancer Subtype Clustering Model Based on Multiomics Data and Pathway and Motif Information
Advanced Data Mining and ApplicationsAbstractMultiomics compiles data from different genome levels to study the effects of interactions between various omics molecules on disease processes. Integrated analysis of different omics data can more comprehensively evaluate their role in human ...
Alzheimer's disease target selection: a data integration approach
BCB '10: Proceedings of the First ACM International Conference on Bioinformatics and Computational BiologyThe integration of information across carefully selected species and experimental platforms is emerging as a powerful technique for selecting potential therapeutic targets for human diseases: this method enables a holistic view of the experimental ...
Bioinformatics-Based Acquisition of Alzheimer’s Disease Hub Genes
Big Data – BigData 2023AbstractOBJECTIVE: To analyze the functions and pathways of differentially expressed genes (DEGs) between Alzheimer’s disease (AD) patients and normal controls using bioinformatics methods, and to screen Hub genes to provide theoretical support for the ...
Comments