Applied Sciences, Vol. 13, Pages 8980: Gene Association Classification for Autism Spectrum Disorder: Leveraging Gene Embedding and Differential Gene Expression Profiles to Identify Disease-Related Genes

Applied Sciences, Vol. 13, Pages 8980: Gene Association Classification for Autism Spectrum Disorder: Leveraging Gene Embedding and Differential Gene Expression Profiles to Identify Disease-Related Genes

Applied Sciences doi: 10.3390/app13158980

Authors: Apichat Suratanee Kitiporn Plaimas

Identifying genes associated with autism spectrum disorder (ASD) is crucial for understanding the underlying mechanisms of the disorder. However, ASD is a complex condition involving multiple mechanisms, and this has resulted in an unclear understanding of the disease and a lack of precise knowledge concerning the genes associated with ASD. To address these challenges, we conducted a systematic analysis that integrated multiple data sources, including associations among ASD-associated genes and gene expression data from ASD studies. With these data, we generated both a gene embedding profile that captured the complex relationships between genes and a differential gene expression profile (built from the gene expression data). We utilized the XGBoost classifier and leveraged these profiles to identify novel ASD associations. This approach revealed 10,848 potential gene–gene associations and inferred 125 candidate genes, with DNA Topoisomerase I, ATP Synthase F1 Subunit Gamma, and Neuronal Calcium Sensor 1 being the top three candidates. We conducted a statistical analysis to assess the relevance of candidate genes to specific functions and pathways. Additionally, we identified sub-networks within the candidate network to uncover sub-groups of associations that could facilitate the identification of potential ASD-related genes. Overall, our systematic analysis, which integrated multiple data sources, represents a significant step towards unraveling the complexities of ASD. By combining network-based gene associations, gene expression data, and machine learning, we contribute to ASD research and facilitate the discovery of new targets for molecularly targeted therapies.