ETFC

Deep learning-based multi-functional therapeutic peptides prediction wih an imbalance-compensating weighted loss funcation

Introduction

In this paper, we develop a novel deep neural network-based MLC model named ETFC to predict MFPTs. This work has the following advantages over existing methods:
(1) In the ETFC model, semantic-based and position-based embedding block combined with MHSA can capture more peptide sequence information, and text convolutional neural network (TextCNN) could extract the more effective information from peptide sequence.
(2) To handle the imbalance problem in the MLC dataset, we design a novel loss function, termed multi-label focal dice loss (MLFDL), for MLC based on FL and dice loss (DL). MLFDL can dynamically assign weights to labels by exploiting label correlations to improve the prediction performance.
(3) We use the teacher-student-based knowledge distillation to obtain the importance of AA and quantify their contributions towards each of the investigated activities.

The framework of the ETFC method for MFTP prediction is described as follows:

The teacher-student framework for knowledge distillation is exhibited as follows:

Related Files

ETFC

FILE NAME	DESCRIPTION
main.py	the main file of ETFC predictor (include data reading, encoding, and data partitioning)
KD_main.py	the main file of knowledge distillation
train.py	train model
model.py	model construction
util.py	utils used to build models
loss_functions.py	loss functions used to train models
evaluation.py	evaluation metrics (for evaluating prediction results)
dataset	data
result	Models and results preserved during training.
Figs	Saved figures

Installation

• Requirement

  OS：

  • Windows ：Windows10 or later

  • Linux：Ubuntu 16.04 LTS or later

  Python：

  • Python >= 3.6

• Download ETFCto your computer

  git clone https://github.com/xialab-ahu/ETFC.git

• open the dir and install requirement.txt with pip

  cd ETFC
  pip install -r requirement.txt
  

Training and test ETFC model

cd "./ETFC/ETFC"
python main.py

Run ETFC on a new test fasta file

python predictor.py --file test.fasta --out_path result

• --file : input the test file with fasta format

• --out_path: the output path of the predicted results

Contact

Please feel free to contact us if you need any help.