GitHub

DAGs with NO TEARS

[Update] Interested in faster and more accurate structure learning? See our new dagrad library for developing and experimenting with newer differentiable (gradient-based) structure learning methods.
Frontiers

DEG-BRIN-GCN: interpretable graph convolutional framework with differentially expressed genes brain region interaction network prior for AD diagnosis

Due to the intricate dynamic coupling between molecular networks and brain regions, early diagnosis and pathological mechanism analysis of Alzheimer's disease (AD) remain highly challenging. To ...
IEEE

Early Parkinson’s Disease Prediction Using rS-fMRI Functional Connectivity and Autoencoder Graph Convolutional Network

Abstract: Early identification of prodromal Parkinson’s disease (PD) is critical, as interventions at this stage can significantly alter its course. We propose a deep learning framework that combines ...
Scientific Research Publishing

An In-Depth Analysis of Graph Neural Networks and Machine Learning Approaches for Drug Repositioning ()

1 Department of Computer Science and Engineering, Kishoreganj University, Kishoreganj, Bangladesh. 2 Department of Electrical and Computer Engineering, North South University, Dhaka, Bangladesh. 3 ...
Diginomica

Blue Yonder aims for ‘one speed’ supply chains with new AI and knowledge graph updates

Blue Yonder’s latest product updates are a culmination of work that’s been happening behind the scenes. The foundations are now in place for buyers to reconsider how they think about their supply ...
IEEE

Multi-Network Graph Contrastive Learning for Cancer Driver Gene Identification

Abstract: Identifying driver genes contributing to the occurrence and development of cancers plays a critical role in cancer research and treatment. Some recent computational approaches identify ...