future97 Goto Github PK

a-b-relationship

miRNA, circRNA, lncRNA, microbe, protein, drug, disease

adamcl

PyTorch implementation of AdaCML

agaemd

a new computational method to predict miRNA-disease associations

amdecda

amhmda

apegnn

bacpi

A bi-directional attention neural network for compound–protein interaction and binding affinity prediction.

bgf-cmap

bioinfo-move-2022

captum

Model interpretability and understanding for PyTorch

cellcircloc

CellCircLoc: Deep Neural Network for Predicting and Explaining Cell line-specific CircRNA Subcellular Localization

cf_diff

circrna-disease-association

circrna-disease-project4

circrna-diseaseanalysis

利用深度学习模型挖掘CircRNA-Disease之间的关联

clpida

compareexperiment

This is the code and the data of a underview paper named "Benchmarking computational methods for predicting circRNA-disease asssociations", which contains 10 methods and 5 collected datasets. The detail will be introduced after the paper is accepted.

comprehensive-tcga-survival

Comprehensive survival analyses on the TCGA data.

cosmig

Communicative Subgraph Representation Learning for Multi-Relational Inductive Drug-Gene Interaction Prediction

daedl

[ICML 2024] Official code for Uncertainty Estimation by Density Aware Evidential Deep Learning

dce

deepdtnet

deepkegg

DeepKEGG

deeplda

dgcl

disease-mesh-similarity

For each disease, a directed acyclic graph (DAG) is constructed based on hierarchical descriptors, in which nodes represent disease MeSH descriptors (or disease terms) and edges represent the relationship between the current node and its ancestors. Then compare the DAG Similarity.

dismvc

dmfmsf

drug-target

MGNN-Drug-target

fkan-gcf