Autism

Discovering latent subtypes of preterm birth and genetic risk using tensor decomposition on electronic health records

Preterm birth is a syndrome that is triggered by diverse biological pathways and presents with many comorbid diseases. Although twin studies reveal a substantial heritable component, the genetic mechanisms of preterm birth remain poorly understood. We hypothesize that refining the preterm birth phenotype will reveal sub-phenotypes associated with distinct genetic risk factors and potential treatme...

Oxytocin Enhances Social-Emotional Reciprocity in Autism

We evaluated whether oxytocin improves social-emotional reciprocity in children and adolescents with autism spectrum disorder (ASD) by conducting a secondary, hypothesis-driven reanalysis of the SOARS-B trial, the largest randomized clinical trial of intranasal oxytocin to date involving over 272 youth. We used a machine learning approach to construct data-driven composite outcome measures maximal...

CSF Proteomics and Machine Learning Reveal Distinct Stages Across the Alzheimer’s Disease Continuum

Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by heterogeneous pathophysiological changes that begin years before symptoms em...

Modeling nonlinear and interaction effects of spatiotemporal and other non-genetic factors improves phenotypic prediction for complex traits

Adjusting for non-genetic factors can improve genetic association testing and polygenic prediction, yet most studies rely on linear adjustments for a ...

Quantitative EEG-Based Deep Learning for Neonatal Seizure Detection using Conv-LSTM

Neonatal seizures cause significant morbidity and mortality, both acutely and in the long term, contributing to adverse neurodevelopmental outcomes. T...

A precision health approach to medication management in neurodevelopmental conditions: a model development and validation study using four international cohorts

Psychotropic medications are commonly used for children with neurodevelopmental conditions, but their effectiveness varies, making treatment selection...

Prediction of Adolescent Internalizing Disorder Risk: Evidence from the Norwegian Mother, Father, and Child Cohort Study

Internalizing disorders are among the most common psychiatric conditions in adolescence, often associated with long-term adverse outcomes. Early ident...

Scaling genetic discovery for organ volumes using machine learning-assisted imputation and bias-corrected GWAS

MRI-derived organ and tissue volumes are powerful endophenotypes for studying complex disease, but their availability is limited by cost and throughpu...

Rule-out test for autism using machine-learning analysis of molecular temporal dynamics in hair - a multicenter study

Early intervention can improve autism-related outcomes. However, no valid biosignature test exists yet for detecting or excluding autism. In addition,...

REECAP: Contrastive learning of retinal aging reveals genetic loci linking morphology to eye disease

Deep learning foundation models excel at disease prediction from medical images, yet their potential to bridge tissue morphology with the genetic arch...

A Novel Method to Disentangle Tightly Linked Risk and Resilience Genes for Brain Disorders: Application to Alzheimer’s Disease

Genetic risk factors for neuropsychiatric disorders are well documented. However, some individuals with high genetic risk remain unaffected, and the m...

Deep-learning-derived glaucoma-related endophenotypes enable novel genome-wide genetic and functional discovery

The genetic architecture of primary open-angle glaucoma (POAG), a leading cause of irreversible blindness, remains largely unexplained due to the reli...

Incorporating Dietary Information to Enhance Polygenic Prediction Models with Applications to Body Mass Index and Type 2 Diabetes

Polygenic predictors can enhance screening for biomedical conditions, such as metabolism-related traits and diseases, but explain limited phenotypic v...

Shared genetic architecture of brain age gap across 30 cohorts worldwide

Deviations from normative brain ageing trajectories are linked to a wide range of adverse health outcomes. A number of brain age prediction models hav...

ML-Guided GWAS Reveals Genetic Architectures for MASLD for Overweight and Lean Individuals in the All of Us Cohort

Metabolic dysfunction-associated steatotic liver disease (MASLD) arises from excessive hepatic fat accumulation that triggers inflammation and liver i...

Exploring Machine Learning Models to Uncover Pathways in ALS Pathogenesis Using Immunohistochemical Features

Amyotrophic Lateral Sclerosis (ALS) is a degenerative disease of motor neurons that leads to muscle wasting, paralysis, and death, with an average lif...

Heterogeneous epigenetic variation converges on splicing dysregulation in opioid addiction

Disease heterogeneity presents a major challenge for genetic and epigenetic dissection of complex traits. Neuropsychiatric traits, such as opioid use ...

A proteogenomic atlas of idiopathic pulmonary arterial hypertension reveals sex-dimorphic mechanisms and potential novel therapeutic targets

Current circulating biomarkers for idiopathic pulmonary arterial hypertension (IPAH) lack specificity for preclinical detection and fail to capture th...

Machine learning augmented genome-wide meta-analysis of prescription opioid use in 860,000 individuals

Opioid analgesics are widely prescribed for pain, yet individuals vary markedly in their patterns of medical opioid use, influencing the risk of prolo...

The translational power of Alzheimer's-based organoid models in personalized medicine: an integrated biological and digital approach embodying patient clinical history.

Alzheimer's disease (AD) is a complex neurodegenerative condition characterized by a multifaceted interplay of genetic, environmental, and pathologica...