Latest AI and machine learning research in autism for healthcare professionals.
To prevent clozapine-induced agranulocytosis (CIA), patients’ white blood cell counts are closely monitored, with treatment stopped if the absolute neutrophil count (ANC) drops below 1.5×109/L. While effective, this approach has a high rate of false positives. This study aimed to develop a machine learning (ML) decision-making tool to better predict CIA risk using pattern-based criteria (two conse...
Recent work leveraging artificial intelligence has offered promise to dissect disease heterogeneity by identifying complex intermediate brain phenotypes, called dimensional neuroimaging endophenotypes (DNEs). We advance the argument that these DNEs capture the degree of expression of respective neuroanatomical patterns measured, offering a dimensional neuroanatomical representation for studying di...
Autism subtypes, including general Autism Spectrum Disorder (ASD) and Asperger Syndrome (AS), exhibit distinct neural connectivity patterns. This stud...
Autism spectrum disorder(ASD) is a neurological condition marked by impaired communication abilities, social detachment, and repetitive behaviors in i...
The polygenic risk score genetic quantitative ultrasound speed of sound (gSOS) was developed using machine learning algorithms in adults of European a...
Introduction: Achieving equity in translational precision medicine requires the integration of genomic, clinical, and social determinants of health (S...
A variety of common and rare genetic factors have been implicated in the development of amyotrophic lateral sclerosis (ALS), and the evidence is that ...
Although genetic variant effects often interact non-additively, strategies to uncover epistasis remain in their infancy. Here, we develop low-signal s...
Rare diseases collectively affect 5% of the population. However, fewer than 50% of rare disease patients receive a molecular diagnosis after whole gen...
Alzheimer’s disease (AD) and Schizophrenia (SCZ) exhibit overlapping clinical features and biological mechanisms, but the extent of their shared genet...
Population screening for rare genetic diseases is limited by the high cost of next- generation sequencing. Double-batched sequencing (DoBSeq) is a cos...
Alzheimer’s disease (AD), particularly late-onset AD, stands as the most prevalent neurodegenerative disorder globally. Owing to its substantial herit...
Cardiometabolic diseases are multifactorial disorders influenced by numerous genetic variants and their complex interactions. Although recent studies ...
Predicting the early onset of dementia due to Alzheimer’s Disease (AD) has major implications for timely clinical management and outcomes. Current dia...
Polygenic risk scores (PRSs) serve as quantitative metrics of genetic liability for various conditions. Traditionally calculated as an effect size wei...
Allostatic load refers to the cumulative burden of stress and life events that involve the interaction of various physiological systems at differing l...
Sickle cell disease (SCD) is one of the most prevalent hemoglobinopathies worldwide, particularly in regions with high genetic predisposition. Early d...
Autism spectrum disorder (ASD) is associated with difficulties in emotion recognition and regulation, which complicates clinical support and treatment...
Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex, heterogeneous, and systemic disease defined by a suite of symptoms, includin...
Understanding the genetic architecture of schizophrenia (SCZ) is invaluable for the development of personalized treatment. In three independent cohort...