Multi-modal co-attention for drug-target interaction annotation and Its Application to SARS-CoV-2

Last update: Nov 14, 2022

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Deep Learning CoaDTI

Overview

CoaDTI

Multi-modal co-attention for drug-target interaction annotation and Its Application to SARS-CoV-2

Abstract

Environment

The test was conducted in the linux server with GTX2080Ti and the running environment is as follows:

python 3.7
pytorch 1.7.1
rdkit
pytorch geometric 1.6.3
Cuda 10.0.130

Data

Human dataset

C.elegans dataset

Binding_DB dataset

How to run

CoaDTI

Run ./code/data_prepare.py to preprocess the dataset.
Run ./code/train.py to train the CoaDTI.

CoaDTI-pro

Run ./code/data_prepare.py to preprocess the dataset.
Run ./code/train.py to train the CoaDTI-pro.

Owner

Layne_Huang

Bioinformatics, BioNLP

GitHub Repository

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Multi-modal co-attention for drug-target interaction annotation and Its Application to SARS-CoV-2

Related tags

Overview

CoaDTI

Abstract

Environment

Data

Human dataset

C.elegans dataset

Binding_DB dataset

How to run

CoaDTI

CoaDTI-pro

Owner

Layne_Huang

Image-to-image regression with uncertainty quantification in PyTorch

This repository lets you interact with Lean through a REPL.

An offline deep reinforcement learning library

Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning

face_recognization (FaceNet) + TFHE (HNP) + hand_face_detection (Mediapipe)

Pytorch implementation for M^3L

[ICLR 2021] "CPT: Efficient Deep Neural Network Training via Cyclic Precision" by Yonggan Fu, Han Guo, Meng Li, Xin Yang, Yining Ding, Vikas Chandra, Yingyan Lin

my graduation project is about live human face augmentation by projection mapping by using CNN

PyTorch implementation of the paper Ultra Fast Structure-aware Deep Lane Detection

PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.

ExCon: Explanation-driven Supervised Contrastive Learning

PyTorch implementation of the Deep SLDA method from our CVPRW-2020 paper "Lifelong Machine Learning with Deep Streaming Linear Discriminant Analysis"

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

"SinNeRF: Training Neural Radiance Fields on Complex Scenes from a Single Image", Dejia Xu, Yifan Jiang, Peihao Wang, Zhiwen Fan, Humphrey Shi, Zhangyang Wang

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