Autism

dFCExpert: Learning Dynamic Functional Connectivity Patterns with Modularity and State Experts

Characterizing brain dynamic functional connectivity (dFC) patterns from functional Magnetic Resonance Imaging (fMRI) data is of paramount importance in imaging neuroscience and medicine. Recently, many graph neural network (GNN) models, combined with transformers or recurrent neural networks (RNNs), have shown great potential for modeling the dFC patterns. However, these methods face challenges i...

pyRootHair: Machine Learning Accelerated Software for High-Throughput Phenotyping of Plant Root Hair Traits

Root hairs play a key role in plant nutrient and water uptake. Historically, root hair traits have been largely quantified manually. As such, this process has been laborious and low-throughput. However, given their importance for plant health and development, high-throughput quantification of root hair morphology could help underpin rapid advances in the genetic understanding of these traits. With...

Inferring the landscapes of mutation and recombination in the common marmoset (Callithrix jacchus) in the presence of twinning and hematopoietic chimerism

The common marmoset is an important model in biomedical and clinical research, particularly for the study of age-related, neurodegenerative, and neuro...

Machine Learning Resolves Functional Phenotypes and Therapeutic Responses in KCNQ2 Developmental Epileptic Encephalopathy iPSC Models

Pathogenic KCNQ2 variants are associated with developmental and epileptic encephalopathy (KCNQ2-DEE), a devastating disorder characterized by neonatal...

Predicting flowering time using integrated morphophysiological and genomic data with machine learning models

Indigenous Cannabis Sativa populations have adapted to diverse environments, resulting in genetic and phenotypic diversity. Understanding the mechanis...

Colorectal cancer heterogeneity co-evolves with tumor architecture to determine disease outcome

Intratumoral heterogeneity, originating from genetic, epigenetic, and phenotypic cellular diversity, is pervasive in cancer. As these heterogeneous st...

EpiPred: A gene-specific machine learning model for classifying missense variants in the epilepsy-related gene STXBP1

Missense variants in the STXBP1 gene are a frequent cause of early-onset developmental and epileptic encephalopathies and related neurodevelopmental d...

Scratcher: An automated machine-vision tool for dissecting the neural basis of itch

Itch or pruritus invokes a specific reflexive and repetitive directed nocifensive behavioural response, known as scratching. Recent decades have revea...

Large-Scale Neural Network Compensation Underlying Camouflaging in Trait Autism and Its Potential Mental Health Costs

Social camouflaging refers to strategies to hide or compensate for social difficulties, often at a significant mental health cost, and is particularly...

A systematic protocol to identify “clinical controls” for pediatric neuroimaging research from clinically acquired brain MRIs

Progress at the intersection of artificial intelligence and pediatric neuroimaging necessitates large, heterogeneous datasets to generate robust and g...

Neural Network-Enhanced Investigation of Ferroptosis and Druggability in Early-Onset Alzheimer’s Disease

Alzheimer’s disease (AD) is a complex neurodegenerative disorder which is multifactorial in nature. Some of its characteristics are slow cognitive dec...

Modeling differences in neurodevelopmental maturity of the reading network using support vector regression on functional connectivity data

The construction of growth charts trained to predict age or developmental deviation (the ‘brain-age index’) based on structural/functional properties ...

Drosophila Video-assisted Activity Monitor (DrosoVAM): a versatile method for behavior monitoring

Drosophila melanogaster has been a pioneering model system for investigations into the genetic bases of behavior. Studies of circadian activity were s...

Decoding the interconnected splicing patterns of hepatitis B virus and host using large language and deep learning models

Hepatitis B virus (HBV) infection causes approximately one million deaths annually and remains a major driver of hepatocellular carcinoma. Despite its...

Improving polygenic risk prediction performance through integrating electronic health records by phenotype embedding

Large-scale biobanks provide comprehensive electronic health records (EHRs) that capture detailed clinical phenotypes, potentially enhancing disease r...

Can large language models reliably extract human disease genes from full-text scientific literature?

Manual extraction of high-fidelity gene-disease-phenotype information from human genetics literature is a labor-intensive task that requires trained h...

The genetic architecture of the human bZIP interaction network

Generative biology holds the promise to transform our ability to design and understand living systems by creating novel proteins, pathways, and organi...

A genetic algorithm for self-supervised models of oscillatory neurodynamics

Predictive processing theories propose that the brain supervises itself, to build an internal model of its environment. This internal model emerges by...

In Silico Design of APOE ɛ4 Interaction Inhibitor Peptides for Alzheimer’s Disease

Protein-protein interactions (PPIs) are essential for cellular functions, and their aberrant formation contributes to neurodegenerative diseases. Alzh...

Control of a Bi-Stable Genetic System via Parallelized Reinforcement Learning

Achieving real-time control of genetic systems is critical for improving the reliability, efficiency, and reproducibility of biological research and e...