Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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,  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,  use a stacked denoising auto-encoder to train our regression model in order to extract useful features, and utilize the multilayer perceptron for backpropagation.  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.
['Rui Xie', 'Andrew Quitadamo', 'Jianlin Cheng', 'Xinghua Shi']
I am writing a research paper on a idea A predictive model of gene expression using a deep learning framework
[{"from": "human", "value": "I am writing a research paper on a idea A predictive model of gene expression using a deep learning framework"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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,  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,  use a stacked denoising auto-encoder to train our regression model in order to extract useful features, and utilize the multilayer perceptron for backpropagation.  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."}]