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

This repository contains the exercises and its solution contained in the book "An Introduction to Statistical Learning" in python.

Distributed Asynchronous Hyperparameter Optimization better than HyperOpt.

Lunar is a neural network aimbot that uses real-time object detection accelerated with CUDA on Nvidia GPUs.

Generalized Proximal Policy Optimization with Sample Reuse (GePPO)

Demonstrates how to divide a DL model into multiple IR model files (division) and introduce a simplest way to implement a custom layer works with OpenVINO IR models.

Highly comparative time-series analysis

(Preprint) Official PyTorch implementation of "How Do Vision Transformers Work?"

The Agriculture Domain of ERPNext comes with features to record crops and land

Evaluating Privacy-Preserving Machine Learning in Critical Infrastructures: A Case Study on Time-Series Classification

Tensorflow implementation of DeepLabv2

Model Quantization Benchmark

Semantic Scholar's Author Disambiguation Algorithm & Evaluation Suite

🌈 PyTorch Implementation for EMNLP'21 Findings "Reasoning Visual Dialog with Sparse Graph Learning and Knowledge Transfer"

It's final year project of Diploma Engineering. This project is based on Computer Vision.

Implementation of Diverse Semantic Image Synthesis via Probability Distribution Modeling

Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

BlueFog Tutorials

The Python code for the paper A Hybrid Quantum-Classical Algorithm for Robust Fitting

Source code for our Paper "Learning in High-Dimensional Feature Spaces Using ANOVA-Based Matrix-Vector Multiplication"

A web porting for NVlabs' StyleGAN2, to facilitate exploring all kinds characteristic of StyleGAN networks