DeepMicroFinder

DeepMicroFinder is a deep learning framework, which integrated the neural network and transfer learning and could effectively reduce the regional effects for microbial-based cross-regional diagnosis of T2D.

• /fig1 # The figure for illustrating the framework of DeepMicroFinder
• /fig2 # The data for conducting the GBTM grouping trajectory
• /model # The DNN models of GGMP and SGMP, and the ontology file
• supplementary_figures_codes # General R scripts used in this study

Get and use

To learn how to install the model and how to use it, click here

Example

The example data for DeepMicroFinder:

Species abundance tables(reference format): shandong_train.tsv shandong_test.tsv
Disease models: GGMP_Disease_Model.h5

Transfer learning

• Transfer the knowledge of shandong to the guandong DNN model for better performance in disease diagnosis on shandong. You'll see running log and training process in the printed message.

expert transfer -i shandong_trainCM.h5 -l shandong_train_labels.h5 -t ontology.pkl -m GGMP_Disease_Model.h5  -o SGMP_Disease_Model 

Search

• Search the test set of shandong against the transferred DNN model.

expert search -i shandong_testCM.h5 -m SGMP_Disease_Model.h5 -o Search_shandong

Evaluation

• Evaluate the performance of the Transferred DNN model. You'll obtain a performance report.

expert evaluate -i Search_shandong -l shandong_test_labels.h5 -o Evaluation

Maintainer

Name	Email	Organization
Nan Wang	[email protected]	Phd student, School of Life Science and Technology, Huazhong University of Science & Technology
Kang Ning	[email protected]	Professor, School of Life Science and Technology, Huazhong University of Science & Technology