Predicting lncRNA–protein interactions based on graph autoencoders and collaborative training

Last update: Nov 29, 2022

Related tags

Overview

Predicting lncRNA–protein interactions based on graph autoencoders and collaborative training

Code for our paper "Predicting lncRNA–protein interactions based on graph autoencoders and collaborative training" (IEEE BIBM 2021)

Requirements

The code has been tested running under Python 3.7.4, with the following packages and their dependencies installed:

numpy==1.16.5
pytorch==1.3.1
sklearn==0.21.3

Usage

git clone https://github.com/zhanglabNKU/LPIGAC.git
cd LPIGAC
python fivefoldcv.py

Options

We adopt an argument parser by package argparse in Python, and the options for running code are defined as follow:

parser = argparse.ArgumentParser()
parser.add_argument('--no-cuda', action='store_true', default=False,
                    help='Disables CUDA training.')
parser.add_argument('--seed', type=int, default=1, help='Random seed.')
parser.add_argument('--epochs', type=int, default=300,
                    help='Number of epochs to train.')
parser.add_argument('--lr', type=float, default=0.01,
                    help='Learning rate.')
parser.add_argument('--weight_decay', type=float, default=1e-7,
                    help='Weight decay (L2 loss on parameters).')
parser.add_argument('--hidden', type=int, default=144,                    help='Dimension of representations')
parser.add_argument('--alpha', type=float, default=0.5,
                    help='Weight between lncRNA space and protein space')
parser.add_argument('--beta', type=float, default=1.0,
                    help='Hyperparameter beta')

args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()

Data

Files of data are listed as follow:

LncRNAName.txt includes the names of all lncRNAs.
ProteinName.txt includes the names of all proteins.
interaction.txt is a matrix Y that shows lncRNA-protein associations. Y[i,j]=1 if lncRNA i and protein j are known to be associated, otherwise 0.
protfeat.txt is the feature matrix of proteins.
rnafeat.txt is the feature matrix of lncRNAs.

Citation

@inproceedings{jin2021lpigac,
    author = {Jin, Chen and Shi, Zhuangwei and Zhang, Han and Yin, Yanbin},
    title = {Predicting lncRNA–protein interactions based on graph autoencoders and collaborative training},
    year = {2021},
    booktitle = {IEEE International Conference on Bioinformatics and Biomedicine (BIBM)},
}

Predicting lncRNA–protein interactions based on graph autoencoders and collaborative training

Related tags

Overview

Predicting lncRNA–protein interactions based on graph autoencoders and collaborative training

Requirements

Usage

Options

Data

Citation

Owner

zhanglabNKU

Equivariant Imaging: Learning Beyond the Range Space

Joint project of the duo Hacker Ninjas

Code of Periodic Activation Functions Induce Stationarity

A Deep learning based streamlit web app which can tell with which bollywood celebrity your face resembles.

This repository implements variational graph auto encoder by Thomas Kipf.

Geometric Algebra package for JAX

Code for ECCV 2020 paper "Contacts and Human Dynamics from Monocular Video".

Code to reproduce the results in the paper "Tensor Component Analysis for Interpreting the Latent Space of GANs".

A Runtime method overload decorator which should behave like a compiled language

DeepLM: Large-scale Nonlinear Least Squares on Deep Learning Frameworks using Stochastic Domain Decomposition (CVPR 2021)

Code for paper ECCV 2020 paper: Who Left the Dogs Out? 3D Animal Reconstruction with Expectation Maximization in the Loop.

Fast and Context-Aware Framework for Space-Time Video Super-Resolution (VCIP 2021)

The Rich Get Richer: Disparate Impact of Semi-Supervised Learning

Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.

KoCLIP: Korean port of OpenAI CLIP, in Flax

Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm

Code for "Contextual Non-Local Alignment over Full-Scale Representation for Text-Based Person Search"

Audio-Visual Generalized Few-Shot Learning with Prototype-Based Co-Adaptation

Neural Dynamic Policies for End-to-End Sensorimotor Learning

cisip-FIRe - Fast Image Retrieval