Molecular Classification Using Hyperdimensional Graph Classification

arXiv:2403.12307v1 Announce Type: new
Abstract: Our work introduces an innovative approach to graph learning by leveraging Hyperdimensional Computing. Graphs serve as a widely embraced method for conveying information, and their utilization in learning has gained significant attention. This is notable in the field of chemoinformatics, where learning from graph representations plays a pivotal role. An important application within this domain involves the identification of cancerous cells across diverse molecular structures.
We propose an HDC-based model that demonstrates comparable Area Under …

abstract arxiv attention classification computing cs.ai cs.lg cs.ne graph graph learning graphs information pivotal q-bio.qm role serve type work