DeepASD: a deep adversarial-regularized graph learning method for ASD diagnosis with multimodal data.

ABSTRACT: Autism Spectrum Disorder (ASD) is a prevalent neurological condition with multiple co-occurring comorbidities that seriously affect mental health. Precisely diagnosis of ASD is crucial to intervention and rehabilitation. A single modality may not fully reflect the complex mechanisms underlying ASD, and combining multiple modalities enables a more comprehensive understanding. Here, we propose, DeepASD, an end-to-end trainable regularized graph learning method for ASD prediction, which incorporates heterogeneous multimodal data and latent inter-patient relationships to better understand the pathogenesis of ASD. DeepASD first learns cross-modal feature representations through a multimodal adversarial-regularized encoder, and then constructs adaptive patient similarity networks by leveraging t

SUBMITTER: Chen W 

PROVIDER: S-EPMC11401938 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature