## 4 Discussion  
Based on the findings of our study, we have drawn several significant conclusions. We discovered the remarkable power of data integration. Based on the utilization of the differential gene expression profile, we achieved commendable performance in association classification. Furthermore, the integration of network information from the gene embedding profile further enhanced the classification performance. Although utilizing the gene embedding profile alone resulted in a slightly lower performance, the integration of data from multiple sources distinctly enhanced the overall effectiveness of our study. Figure 7: Three sub-networks created by clustering the candidate network: (**a**) the sub-network consists of 53 genes and 87 associations, (**b**) the sub-network consists of 33 genes and 60 associations, and (**c**) the sub-network consists of 64 genes and 124 associations. The red nodes represent known ASD-related genes and the green nodes represent new candidate genes. The bold lines represent known associations and the dashed lines represent unknown associations. Consequently, we can confidently conclude that data integration holds immense potential for enhancing performance in association classification, as is evidenced by our results. In addition to utilizing diverse data characteristics, such as the differential gene expression profile and the gene embedding profile, we also integrated data concerning associations between genes related to ASD from the AutDB database [51; 52] and information on known ASD-related genes from the SFARI and AutismKB databases to identify significant genes. It was observed that genes found in the association database occasionally did not appear in the ASD-related gene databases (SFARI and AutismKB). This could be due to our stringent selection process, whereby we only classified core genes and genes with high confidence scores from the databases as known ASD-related genes. Nevertheless, both datasets played a valuable role in identifying potential ASD-related genes within the sub-networks obtained through candidate network clustering. From the results of the candidate gene inference, we made an intriguing discovery: the top three genes, namely _TOP1_, _ATP5F1C_, and _NCS1_, emerged as high-frequency candidates in our association predictions. It has been reported that inhibiting _TOP1_ leads to the downregulation of long genes associated with synaptic function in neurons [53]. The study suggested that _TOP1_ mutations may contribute to ASD and other neurodevelopmental disorders. _ATP5F1C_, which encodes a subunit of mitochondrial ATP synthase [54], caught our attention. It is involved in the conversion of physical energy into chemical energy [55; 56; 57]. While direct evidence linking _ATP5F1C_ to ASD is lacking, it has been reported that alterations in energy homeostasis have been observed in individuals with ASD [58], making _ATP5F1C_ a potential gene of interest. Additionally, _NCS1_ has been associated with ASD because a missense mutation (R102Q) in the human _NCS1_ protein was identified in an autistic patient [59]. This further highlights the potential relevance of _NCS1_ to ASD. _TOP1_, _ATP5F1C_, and _NCS1_ are noteworthy candidate genes that exhibit promising connections to ASD and warrant further investigation and exploration in relation to their potential associations with ASD. Moreover, [PERSON] and colleagues [60] conducted an in-depth study on the gene expression profile of the brain cortex, analyzing 15 cases of individuals with ASD and 15 control subjects using RNA-Seq transcriptomics. Their comprehensive meta-analysis revealed the presence of 1567 differentially expressed genes. We compared these to our candidates and identified six specific genes: _ACOT7_, _DNMI_, _DYNCII1_, _GAD2_, _GPC2_, and _PSMB8_. These genes have drawn particular interest and merit further investigation in the context of ASD.