DMLF: Enzyme Commission Number Predicting and Benchmarking with Multi-agent Dual-core Learning

This repo contains source codes for a EC prediction tool namely ECRECer, which is an implementation of our paper: 「ECRECer: Enzyme Commission Number Recommendation and Benchmarking based on Multiagent Dual-core Learning」

Detailed information about the framework can be found in our paper

Shi, Z., Yuan, Q., Wang, R., Li, H., Liao, X., & Ma, H. (2022). ECRECer: Enzyme Commission Number Recommendation and Benchmarking based on Multiagent Dual-core Learning. arXiv preprint arXiv:2202.03632.

For simply use our tools to predict EC numbers, pls visit our web service at https://ecrecer.biodesign.ac.cn

To re-implement our experiments or offline use, pls use read the details below:

Prerequisites

• Python >= 3.6
• Sklearn
• Xgboost
• conda
• jupyter lab
• ...

Create conda env use env.yaml

conda env create -f env.yaml

Preprocessing

Download and prepare the data set use the.

prepare_task_dataset.ipynb

Step by step benchmarking

Task 1: Enzyme or None-Enzyme Prediction

./tasks/task1.ipynb

Task 2: Polyfunctional Enzyme Prediction

./tasks/task2.ipynb

Task 3: EC Number Prediction

./tasks/task3.ipynb

High throughput benchmarking

Train

python benchmark_train.py

Test

python benchmark_test.py

Evaluation

python benchmark_evaluation.py

Production

python production.py -i input_fasta_file -o output_tsv_file -mode [p|r] -topk 5

Other details are coming soon...

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