Name
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.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3319619.3321920 | GECCO |
Keywords | Field | DocType |
Multiomics, Data Integration, Curse of Dimensionality, Neural Networks, Biological Application | Data integration,Computer science,Artificial intelligence,Artificial neural network,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4503-6748-6 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pankhuri Singhal | 1 | 0 | 0.34 |
| Shefali S Verma | 2 | 1 | 3.73 |
| Scott M. Dudek | 3 | 206 | 26.27 |
| Marylyn D Ritchie | 4 | 0 | 1.01 |