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

Related tags

Deep LearningLPIGAC
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)},
}
Owner
zhanglabNKU
Data Mining Lab. Prof. Han Zhang
zhanglabNKU
This repository contains the segmentation user interface from the OpenSurfaces project, extracted as a lightweight tool

OpenSurfaces Segmentation UI This repository contains the segmentation user interface from the OpenSurfaces project, extracted as a lightweight tool.

Sean Bell 66 Jul 11, 2022
wmctrl ported to Python Ctypes

work in progress wmctrl is a command that can be used to interact with an X Window manager that is compatible with the EWMH/NetWM specification. wmctr

Iyad Ahmed 22 Dec 31, 2022
Code for "Learning Skeletal Graph Neural Networks for Hard 3D Pose Estimation" ICCV'21

Skeletal-GNN Code for "Learning Skeletal Graph Neural Networks for Hard 3D Pose Estimation" ICCV'21 Various deep learning techniques have been propose

37 Oct 23, 2022
Self-Adaptable Point Processes with Nonparametric Time Decays

NPPDecay This is our implementation for the paper Self-Adaptable Point Processes with Nonparametric Time Decays, by Zhimeng Pan, Zheng Wang, Jeff M. P

zpan 2 Sep 24, 2022
Spectralformer: Rethinking hyperspectral image classification with transformers

Spectralformer: Rethinking hyperspectral image classification with transformers Danfeng Hong, Zhu Han, Jing Yao, Lianru Gao, Bing Zhang, Antonio Plaza

Danfeng Hong 102 Dec 29, 2022
Code for our paper Aspect Sentiment Quad Prediction as Paraphrase Generation in EMNLP 2021.

Aspect Sentiment Quad Prediction (ASQP) This repo contains the annotated data and code for our paper Aspect Sentiment Quad Prediction as Paraphrase Ge

Isaac 39 Dec 11, 2022
Extracting and filtering paraphrases by bridging natural language inference and paraphrasing

nli2paraphrases Source code repository accompanying the preprint Extracting and filtering paraphrases by bridging natural language inference and parap

Matej Klemen 1 Mar 09, 2022
NAS Benchmark in "Prioritized Architecture Sampling with Monto-Carlo Tree Search", CVPR2021

NAS-Bench-Macro This repository includes the benchmark and code for NAS-Bench-Macro in paper "Prioritized Architecture Sampling with Monto-Carlo Tree

35 Jan 03, 2023
Code and Experiments for ACL-IJCNLP 2021 Paper Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answering.

Code and Experiments for ACL-IJCNLP 2021 Paper Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answering.

Sidd Karamcheti 50 Nov 16, 2022
Implement of "Training deep neural networks via direct loss minimization" in PyTorch for 0-1 loss

This is the implementation of "Training deep neural networks via direct loss minimization" published at ICML 2016 in PyTorch. The implementation targe

Cuong Nguyen 1 Jan 18, 2022
Pytorch implementation for "Density-aware Chamfer Distance as a Comprehensive Metric for Point Cloud Completion" (NeurIPS 2021)

Density-aware Chamfer Distance This repository contains the official PyTorch implementation of our paper: Density-aware Chamfer Distance as a Comprehe

Tong WU 93 Dec 15, 2022
PyTorch implementation of "ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context" (INTERSPEECH 2020)

ContextNet ContextNet has CNN-RNN-transducer architecture and features a fully convolutional encoder that incorporates global context information into

Sangchun Ha 24 Nov 24, 2022
Multi-atlas segmentation (MAS) is a promising framework for medical image segmentation

Multi-atlas segmentation (MAS) is a promising framework for medical image segmentation. Generally, MAS methods register multiple atlases, i.e., medical images with corresponding labels, to a target i

NanYoMy 13 Oct 09, 2022
code for EMNLP 2019 paper Text Summarization with Pretrained Encoders

PreSumm This code is for EMNLP 2019 paper Text Summarization with Pretrained Encoders Updates Jan 22 2020: Now you can Summarize Raw Text Input!. Swit

Yang Liu 1.2k Dec 28, 2022
Unofficial implementation of the Involution operation from CVPR 2021

involution_pytorch Unofficial PyTorch implementation of "Involution: Inverting the Inherence of Convolution for Visual Recognition" by Li et al. prese

Rishabh Anand 46 Dec 07, 2022
Bulk2Space is a spatial deconvolution method based on deep learning frameworks

Bulk2Space Spatially resolved single-cell deconvolution of bulk transcriptomes using Bulk2Space Bulk2Space is a spatial deconvolution method based on

Dr. FAN, Xiaohui 60 Dec 27, 2022
3D ResNet Video Classification accelerated by TensorRT

Activity Recognition TensorRT Perform video classification using 3D ResNets trained on Kinetics-400 dataset and accelerated with TensorRT P.S Click on

Akash James 39 Nov 21, 2022
This is a vision-based 3d model manipulation and control UI

Manipulation of 3D Models Using Hand Gesture This program allows user to manipulation 3D models (.obj format) with their hands. The project support bo

Cortic Technology Corp. 43 Oct 23, 2022
Code for the paper: Adversarial Training Against Location-Optimized Adversarial Patches. ECCV-W 2020.

Adversarial Training Against Location-Optimized Adversarial Patches arXiv | Paper | Code | Video | Slides Code for the paper: Sukrut Rao, David Stutz,

Sukrut Rao 32 Dec 13, 2022
A pre-trained model with multi-exit transformer architecture.

ElasticBERT This repository contains finetuning code and checkpoints for ElasticBERT. Towards Efficient NLP: A Standard Evaluation and A Strong Baseli

fastNLP 48 Dec 14, 2022