Structural Deep Brain Network Mining
Shen Wang, Lifang He, Bokai Cao, Chun-Ta Lu, Philip S. Yu, Ann Barnett Ragin · 2017
Mining from neuroimaging data is becoming increasingly popular in the field of healthcare and bioinformatics, due to its potential to discover clinically meaningful structure patterns that could facilitate the understanding and diagnosis of neurological and neuropsychiatric disorders. Most recent research concentrates on applying subgraph mining techniques to discover connected subgraph patterns in the brain network. However, the underlying brain network structure is complicated. As a shallow linear model, subgraph mining cannot capture the highly non-linear structures, resulting in sub-optimal patterns. Therefore, how to learn representations that can capture the highly non-linearity of brain networks and preserve the underlying structures is a critical problem.