Editorial: Molecular advances and applications of machine learning in understanding autism and comorbid psychiatric disorders.

1. Gut Microbiota, Depression, and Neurodevelopmental Dysfunc5on: Lanxiang Liu et al. study uncovers the impact of gut microbiota-dysbiosis on hippocampal gene regula1on, elucida1ng the significant role of molecular dysregula1on in neurodevelopmental dysfunc1on. 2. Cerebellar Dysfunc5on and Au5sm: Xiaofan Yang et al. me1culously examine the implica1ons of Scn8a gene knockout in cerebellar Purkinje cells. The research unveils compromised social interac1on, motor learning, reversal learning, and cerebellar degenera1on, with muta1ons in the SCN8A gene linked to epilepsy, intellectual disability, and ASD.The study by Heba Alateyat et al. employ machine learning models to predict behavior outcomes based on sensory profile scores, shedding light on the intricate interplay between sensory processing abili1es and behavioral paXerns in ASD. Overall, this compendium of research ar1cles synthesizes a rich tapestry of insights into the intricate molecular fabric of ASD and comorbid psychiatric disorders. By comprehending these underlying mechanisms, the prospects for early interven1on and improved outcomes for affected individuals are significantly augmented. It is my sincere hope that this compila1on serves as a pivotal steppingstone toward more precise diagnos1cs, individualized treatments, and enhanced therapeu1c interven1ons in the realm of neurodevelopmental disorders.