seongokryu Goto Github PK

ace-team-dlstudy

Study materials about "Deep Learning for Molecular Applications".

alphafold_pytorch

An implementation of the DeepMind's AlphaFold based on PyTorch for research

attentiondeepmil

Implementation of Attention-based Deep Multiple Instance Learning in PyTorch

augmented-gcn

awesome-single-cell

List of software packages for single-cell data analysis, including RNA-seq, ATAC-seq, etc.

bayesian-deep-learning

Notes and codes of the topic "Bayesian deep learning"

benchmarking-gnns

Repository for benchmarking graph neural networks

ch485---artificial-intelligence-and-chemistry

CH485 - Artificial Intelligence and Chemistry

concretedropout

Code for Concrete Dropout as presented in https://arxiv.org/abs/1705.07832

deep-learning-for-medical-applications

Deep Learning Papers on Medical Image Analysis

deep_mahalanobis_detector

Code for the paper "A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks".

deeplearning-biology

A list of deep learning implementations in biology

ge-vae

Graph Embedding VAE (NeurIPS 2019 Workshop on Graph Representations).

gnn_solubility

google-research

Google AI Research

graph-based-deep-learning-literature

graph-neural-networks

gym-trading

Environment for reinforcement-learning algorithmic trading models

hgraph2graph

Hierarchical Graph-to-Graph Translation for Molecules

ipanda

insilico Pathway Activation Network Decomposition Analysis (iPANDA) package. Owned by Insilico Medicine Inc. iPANDA is a pathway analysis method that aids interpretation of large-scale gene expression data. The method introduced here includes coexpression analysis and gene importance estimation to robustly identify relevant pathways and biomarkers for patient stratification.

kaist-thesis-template

KAIST Thesis Template

laidd_gnn

mixmatch

models

Models and examples built with TensorFlow

mol_reliability

"A Comprehensive study on the reliability of neural prediction models for virtual screening"

mol_reliable_gnn

Source codes for 'A baseline for reliable molecular prediction models via Bayesian learning'

molecular-gat

Molecular-GAT

monn

MONN: a Multi-Objective Neural Network for Predicting Pairwise Non-Covalent Interactions and Binding Affinities between Compounds and Proteins

multiple_instance_learning

Multiple instance learning for weakly supervised learning

mutual_information_and_self-supervised_learning

Notes and tutorials on "Mutual information and self-supervised learning"