Bioinfor DeepSEA

DeepSEA is a model to predicting effects of noncoding variants. This is implemented by tensorflow-2.0 again.

DeepSEA

Model Architecture

CNN + Pool + CNN + Pool + CNN + Dense + Dense

Loss Function

Binary Cross Entropy

Optimization Method

SGD with Momentum (momentum = 0.9)

USAGE

Requirement

We run the code on Ubuntu 18.04 LTS with a GTX 1080ti GPU.

Python (3.7.3) | Tensorflow (2.0.0) | CUDA (10.0) | cuDNN (7.6.0)

Data

You need to first download the training, validation, and testing sets from DeepSEA. You can download the datasets from here. After you have extracted the contents of the tar.gz file, move the 3 .mat files into the ./data/ folder.

Model

The model that trained by myself is available in BAIDU Net Disk here

Preprocess

Because of my RAM limited, I firstly transform the train.mat file to .tfrecord files.

python preprocess.py

Training

Then you can train the model initially.

CUDA_VISIBLE_DEVICES=0 python main.py -e train

Test

When you have trained successfully, you can evaluate the model.

CUDA_VISIBLE_DEVICES=0 python main.py -e test

RESULT

You can get my result in the ./result/ directory.

Loss Curve

Metric

We use two metric to evaluate the model. (AUROC, AUPR)

-	DNase	TFBinding	HistoneMark	All
AUROC	0.8925	0.9071	0.8270	0.8960
AUPR	0.3809	0.2400	0.3277	0.2691

REFERENCE

Predicting effects of noncoding variants with deep learning-based sequence model | Github