Latest AI and machine learning research in autism for healthcare professionals.
Interpreting large-scale singlecell transcriptomic data remains a major challenge for understanding disease mechanisms. Recent single-cell foundation models learn rich representations of gene relationships across millions of cells, yet methods for translating these embeddings into biologically interpretable gene networks remain limited. Here we present scGENet, a computational framework that const...
Objective: Genetic disease is common in Level IV Neonatal Intensive Care Units (NICUs), yet clinicians often struggle to identify infants who would benefit from genetic evaluation. We developed and validated NeoGx, a machine learning (ML) algorithm using electronic health record (EHR) data to predict, early in the NICU stay, which neonates will require genetic evaluation within 18 months of life, ...
The human brain undergoes rapid developmental changes through early life, underpinning the emergence of function but also marking a period of vulnerab...
Purpose Several clinical studies have shown correlations between certain physiological measure-ments and an ASD diagnosis. Such findings, however, hav...
Objective. To develop an interpretable multimodal machine-learning model for risk stratification of the rapid pain progression phenotype in knee osteo...
Background Childhood neurodegenerative disorders are usually rare, genetic, and life-limiting. Whilst targeted approaches present huge potential, sign...
The human immune system is composed of [~]30-50 distinct cell types, each of which can exist in different states of activation or differentiation. Ind...
Genomic prediction of complex traits is limited when phenotype records are restricted and when using linear models. Increasing the amount of phenotypi...
Although grapevine (Vitis spp.) is among the oldest and most economically significant fruit species globally, its genetic improvement faces major bott...
Foundation models for biology achieve impressive pattern recognition on molecular sequences and single-cell transcriptomics, yet they fail to outperfo...
Copper (Cu) is an essential metal involved in neurobiological processes including energy metabolism and neurotransmission, yet dysregulated Cu levels ...
We present NeuronSoup, a neural computation architecture that replaces synchronous layer-by-layer processing with asynchronous, delay-mediated signal ...
Background & Methods: The multifaceted physical nature of heritable cognitive impairment in dementia presents significant challenges for traditional l...
Longitudinal tumor measurements, dropout information, and genetic covariates provide complementary information about treatment response, but integrati...
Portable low-field MRI systems are a promising complement to conventional high-field systems, enabling broader access to MRI. However, correspondence ...
Autism development involves multiple genetic and early-life environmental factors. Studying the placenta's gene expression profile may reveal key mech...
Compact tissue-specific promoters are highly desirable for gene therapy because viral vectors possess limited packaging capacity. However, existing pr...
Molecular testing in hematology requires different assays for disease subgroup identification, risk stratification and selection of appropriate treatm...
Modelling human cortical microcircuitry in vitro requires platforms that recapitulate both the compositional complexity and spatial architecture of de...
Many biological characteristics arise by interactions between more than one biological organism or unit. Fertilization success in sexually reproducing...