## 5 Conclusions  
Our study employed a systematic analysis and data integration approaches to investigate genes associated with ASD. By analyzing the gene association network, integrating gene expression data, and applying a machine learning algorithm, we identified novel ASD associations and potential gene-gene interactions that may have been disregarded in earlier studies. By utilizing our method, we successfully uncovered a promising total of 10,848 associations, all of which demonstrated the highest prediction score. From these associations, 125 candidate genes were inferred to be related to ASD. A statistical analysis allowed us to assess the relevance of candidate genes to specific functions and pathways associated with ASD. The findings of our study have important implications. The candidate gene list exhibited enrichment in essential terms related to cellular components, biological processes, and molecular functions, e.g., the mitochondrial membrane, the regulation of macromolecule biosynthetic processes, cell-matrix adhesion, and kinase binding. Furthermore, our gene list demonstrated enrichment in significant pathways, including neurodegeneration and MHC class II antigen presentation. The enrichment of these terms suggests the involvement of ASD in multiple underlying mechanisms. Lastly, we successfully constructed and clustered a network comprising candidate genes, allowing us to explore the sub-interactions occurring between different groups of genes. Through an analysis of these sub-networks, we were able to identify and explore the presence of novel and promising genes that exhibit links to known ASD-related genes or serve as connecting genes in established associations, even if they were not present in the database of known ASD-related genes. Several genes, such as _TOP1_, _RBM17_, _RPL3_, _SNRPN_, _UBC_, _GTF3C3_, _DDB1_, _SIRT1_, _CLTC_, _TPPRK_, _HCFC1_, _FXR1_, _EEF2K_, _ALDH19A1_, _SYMPK_, and _DGKZ_, emerged as potential candidates associated with ASD. This discovery holds significant potential and serves as a strong justification for further investigation and research. Overall, our approach, combining network-based gene associations, gene expression analysis, and machine learning, offers a comprehensive framework for ASD research. It represents a significant step towards unraveling the complexities of ASD and provides a foundation for further investigations into the development of effective treatments and interventions for this complex disorder. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app13158980/s1](https://www.mdpi.com/article/10.3390/app13158980/s1)]([https://www.mdpi.com/article/10.3390/app13158980/s1](https://www.mdpi.com/article/10.3390/app13158980/s1)), Figure S1: ROC curves from (a) XGBoost with complete association features, (b) XGBoost with association features based on differential expression profile, (c) XGBoost with association features based on gene embedding profile, (d) random forest classifier, (e) naive Bayes classifier, and (f) neural network; Figure S2: Average correlation values of top-ranked features; Table S1: List of hyperparameters for classification models; Table S2: Proportion of association features calculated from the top selected features of the gene embedding profile and differential expression profile; Table S3: The summary of the performance metrics for all classifiers; Table S4: List of 125 candidate genes and their frequencies; Table S5: Enrichment analysis results for cellular components, biological processes, molecular functions, and pathways; Table S6: List of gene associations in sub-networks. Conceptualization, A.S. and K.P. ; formal analysis, A.S. and K.P. ; funding acquisition, A.S.; methodology, A.S. and K.P. ; writing--original draft preparation, A.S.; writing--review and editing, A.S. and K.P. All authors have read and agreed to the published version of the manuscript. This research was funded by [PERSON]'s University of Technology North Bangkok, Contract no. KMUTNB-65-KNOW-16. Not applicable.