Abstract
Deep Learning can significantly benefit cancer proteomics and genomics. In this study, we attempted to determine a set of critical proteins that were associated with the FLT3-ITD mutation in newly-diagnosed acute myeloid leukemia patients. A Deep Learning network consisting of autoencoders formed a hierarchical model from which high-level features were extracted without labeled training data. Dimensional reduction reduced the number of critical proteins from 231 to 20. Deep Learning found an excellent correlation between FLT3-ITD mutation with the levels of these 20 critical proteins (accuracy 97%, sensitivity 90%, and specificity 100%). Our Deep Learning network could hone in on 20 proteins with the strongest association with FLT3-ITD. The results of this study allow for a novel approach to determine critical protein pathways in the FLT3-ITD mutation, and provide proof-of-concept for an accurate approach to model big data in cancer proteomics and genomics.
Original language | English (US) |
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Pages (from-to) | 119-126 |
Number of pages | 8 |
Journal | Annals of clinical and laboratory science |
Volume | 49 |
Issue number | 1 |
State | Published - Jan 1 2019 |
Externally published | Yes |
Keywords
- AML
- Deep Learning
- FLT3-ITD
- Neural Network
- Proteomics
ASJC Scopus subject areas
- General Medicine