Name
Abstract | ||
|---|---|---|
With an unprecedented amount of data available, it is important to explore new methods for developing predictive models to mine this data for scientific discoveries. In this study, we propose a deep learning regression model based on MultiLayer Perceptron and Stacked Denoising Auto-encoder (MLP-SAE) to predict gene expression from genotypes of genetic variation. Specifically, we use a stacked denoising auto-encoder to train our regression model in order to extract useful features, and utilize the multilayer perceptron for backpropagation. We further improve our model by adding a dropout technique to prevent overfitting. Our results on a real genomic dataset show that our MLP-SAE model with dropout outperform Lasso, Random Forests, and MLP-SAE without dropout. Our study provides a new application of deep learning in mining genomics data, and demonstrates that deep learning has great potentials in building predictive models to help understand biological systems. |
Year | Venue | Keywords |
|---|---|---|
2016 | 2016 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM) | Predictive model,Regression model,MLP-SAE |
Field | DocType | ISSN |
Data mining,Data modeling,Regression analysis,Computer science,Lasso (statistics),Multilayer perceptron,Artificial intelligence,Deep learning,Overfitting,Random forest,Bioinformatics,Backpropagation,Machine learning | Conference | 2156-1125 |
Citations | PageRank | References |
0 | 0.34 | 15 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rui Xie | 1 | 0 | 0.34 |
| Andrew Quitadamo | 2 | 2 | 1.15 |
| Jianlin Cheng | 3 | 762 | 53.31 |
| Xinghua Shi | 4 | 209 | 19.00 |