## 4 Results
### Microbiome and Gut-Brain Axis Studies  
This section explores the gut-brain connection in ASD and examines how microbiome data are emerging as a promising biomarker for the disorder through explainable artificial intelligence (XAI) techniques. By integrating AI with microbiome analysis, this study demonstrates the potential of personalized microbiome profiles in diagnosing and understanding ASD, with implications for developing targeted interventions.  
[PERSON] et al. (2024) [PERSON] et al. (2024) conducted a groundbreaking study focused on identifying autism-related bacterial species in the gut microbiome, employing an XAI framework to enhance interpretability. The researchers used the XGBoost algorithm, a powerful machine learning classifier, to analyze microbiome compositions in children with ASD compared to typically developing (TD) controls. XGBoost was chosen for its high performance with complex, high-dimensional data, such as microbiome profiles. To ensure transparency and understand the impact of individual bacterial species on classification, the study utilized SHapley Additive exPlanations (SHAPs), a prominent XAI technique. SHAP values provided detailed insights into the contribution of each bacterial species, allowing researchers to trace the model's decision-making process and observe specific microbiome patterns linked to ASD.  
The study employed unsupervised clustering of SHAP values, which enabled the researchers to identify subgroups within the ASD cohort based on microbiome composition. This approach revealed different bacterial profiles between individuals with ASD, highlighting the heterogeneity within the ASD population and underscoring the importance of personalized approaches. The clustering findings indicated unique gut microbiome compositions, with certain bacterial species playing a more prominent role in some subgroups, further emphasizing the variability in ASD-related microbiome patterns.  
The classification model performed exceptionally well, achieving an area under the receiver operating characteristic (AU-ROC) of 0.965 and an area under the precision-recall curve (AU-PRC) of 0.967. These metrics confirmed the model's reliability and effectiveness in distinguishing ASD cases from TD controls, validating microbiome data as a significant biomarker for ASD. Based on these results, the study concluded that the integration of XAI with microbiome analysis not only improves diagnostic accuracy, but also provides actionable insights into the gut-brain axis, potentially enabling personalized therapeutic strategies tailored to individual microbiome profiles.  
In summary, this study demonstrates the potential of microbiome data as a biomarker for ASD, illustrating how AI and machine learning models, particularly XAI approaches, can reveal meaningful insights into the gut-brain connection. By identifying ASD-related bacterial species and highlighting microbiome diversity among individuals with ASD, this study offers a foundation for future research into the complex interplay between gut health and neurological development. Through the lens of personalized medicine, these findings pave the way for innovative diagnostic and intervention strategies that consider the unique microbiome profiles of each individual.