zhibinlv/iACP-DRLF

Anti-Cancer Peptide Prediction with Deep Representation Learning Features

iACP-DRLF

Anti-Cancer Peptide Prediction with Deep Representation Learning Features
This repository contains the source code and links to the data and pretrained embedding models accompanying the iACP-DRLF paper: Anti-Cancer Peptide Prediction with Deep Representation Learning Features

How to cite the paper?

Zhibin Lv †, Feifei Cui †, Quan Zou , Lichao Zhang and Lei Xu,Anticancer peptides prediction with deep representation learning features,Briefings in Bioinformatics, 2021, doi: 10.1093/bib/bbab008

†Authors with equally contribution

A GPU like NVIDIA RTX2060 is required.

Setup and dependencies

Install in Ubuntu Linux 18.04

Download from http://public.aibiochem.net/iACP-DRLF/

1. *cd iACP-DRLF

2. *pip install -r pip install -r requirements.txt

3. OK. It could run the python script now.

Install from git hub

1. *git clone https://github.com/zhibinlv/iACP-DRLF.git *

2. *cd iACP-DRLF

3. *pip install -r pip install -r requirements.txt

4. *wget bergerlab-downloads.csail.mit.edu/bepler-protein-sequence-embeddings-from-structure-iclr2019/pretrained_models.tar.gz

   *tar -xzvf pretrained_models.tar.gz

   *mv ./pretrained_models/ssa_L1_100d_lstm3x512_lm_i512_mb64_tau0.5_lambda0.1_p0.05_epoch100.sav ./src/PretrainedModel/SSA_embed.model

   or you could downloand SSA_embed.model from http://public.aibiochem.net/iACP-DRLF/src/PretrainedModel/SSA_embed.model

5. OK. It could run the python script now.

Brief tutorial

1. To validate the paper independent test, run the following code.

   python test.py

2. To use iACP-DRLF

python -m {A or M} -i {sequences in FASTA format} -o {output a CSV file}

A is for Alternate dataset trained model

M is for Main dataset trained model