I co-created Graph Neural Networks while at Stanford. I recognized early on that this technology was incredibly powerful.

Recent breakthroughs in artificial intelligence have transformed protein structure biology from a data-limited discipline into a data-driven frontier of ...

Researchers have developed a new tool, bimodularity, that adds directionality to community detection in networks.

Outdated public tools leave policy makers stranded while private companies hold the real insights The post Who Owns Canada’s ...

Data Governance Goals The primary aim of data and analytics (D&A) governance, our research has taught us, is aligning data ...

This research study employed machine learning algorithm in This research study employed a machine learning algorithm in predicting student academic performance in the Data Structures and Algorithm ...

The graph convolutional layer used in this project is the graph convolutional layer (GraphConv), (17) whose theoretical basis is the approximation of spectral graph convolution, allowing the extension ...

The analysis of multiomics biomedical data has become increasingly critical in clinical decision-making for brain diseases, such as Alzheimer's disease (AD). However, the inherent fuzziness of ...

Data Structures and Algorithms Repository Overview Welcome to the Data Structures and Algorithms Repository! My aim for this project is to serve as a comprehensive collection of problems and solutions ...

Data-Structures-Algorithms-DSA- This repository contains implementations of various data structures and algorithms in C++. It includes basic data structures, graph algorithms, sorting methods, tree ...