Autism

X-Cell: Scaling Causal Perturbation Prediction Across Diverse Cellular Contexts via Diffusion Language Models

Causal models of cellular systems hold the promise to empower broad biological discovery, including the systematic identification of novel targets for drug discovery. Predicting how genetic and pathway perturbations reshape gene expression across diverse cellular contexts is a prerequisite for building generalizable cellular foundation models. However, current methods typically fail to extrapolate...

Causal differential expression analysis under unmeasured confounders with causarray

Advances in single-cell sequencing and CRISPR technologies have enabled detailed case-control comparisons and experimental perturbations at single-cell resolution. However, uncovering causal relationships in observational genomic data remains challenging due to selection bias and inadequate adjustment for unmeasured confounders, particularly in heterogeneous datasets. To address these challenges, ...

Neural signatures of impaired semantic contextualization in Autism Spectrum Disorder

Some accounts of the etiology of autism emphasize core impairments in predictive coding, or, more fundamentally, integration of contextual information...

An Interpretable Machine Learning Framework for Non-Small Cell Lung Cancer Drug Response Analysis

Lung cancer is a condition where there is abnormal growth of malignant cells that spread in an uncontrollable fashion in the lungs. Some common treatm...

pertTF: context-aware AI modeling for genome-scale and cross-system perturbation prediction

Predicting genetic perturbation responses at a single-cell level is central to building models for cell state and disease. However, existing approache...

Multi-Task Genetic Algorithm with Multi-Granularity Encoding for Protein-Nucleotide Binding Site Prediction

Accurate identification of protein-nucleotide binding sites is fundamental to deciphering molecular mechanisms and accelerating drug discovery. Howeve...

Multimodal Connectome Fusion via Cross-Attention for Autism Spectrum Disorder Classification Using Graph Learning

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by atypical functional brain connectivity and subtle structural...

Generative AI-based design of hybrid transcriptional activator proteins with new DNA-binding specificity

Transcriptional control arises from the specific recognition of promoter DNA by transcription factors (TFs), forming the basis of cellular information...

A DNA foundation model predicts osteoporosis risk genes without proximity bias

Targets supported by human genetic associations are more than twice as likely to progress from clinical development to approval. Genome-wide associati...

Predicting targeted- and immunotherapeutic response outcomes in melanoma with single-cell Raman Spectroscopy and AI

Identifying predictive biomarkers of immunotherapeutic response in melanoma remains an outstanding challenge. Existing transcriptomic and proteomic pr...

Distinct cellular DNA methylation mechanisms underlie common and rare genetic risk for brain disorders

Noncoding genetic variation contributes to brain disorder risk, but the mechanisms through which it acts in specific brain cell types remain unclear. ...

Identification and Developmental Analysis of the Facial Characteristics Associated with Sickle Cell Disease using Machine Learning

Background Sickle cell disease (SCD) is a common inherited genetic disorder and contributor to global childhood mortality and morbidity. In the Democr...

Peer Support in Online Discussions of Male Infertility: A Natural Language Processing Study of Reddit

Infertility generates profound psychological and social distress for both women and men, yet mens communicative experiences remain comparatively under...

BrainSTR: Spatio-Temporal Contrastive Learning for Interpretable Dynamic Brain Network Modeling

Dynamic functional connectivity captures time-varying brain states for better neuropsychiatric diagnosis and spatio-temporal interpretability, i.e., i...

Geometric Brain Signatures for Diagnosing Rare Hereditary Ataxias and Predicting Function

Hereditary cerebellar ataxias (HCAs) are rare neurodegenerative disorders characterised by progressive motor impairment and overlapping clinical pheno...

Uncovering genetic mechanisms underlying trait variation in switchgrass using explainable artificial intelligence

Uncovering the genetic architecture of quantitative traits is challenging because polygenic control yields small individual gene effects and because g...

Popformer: Learning general signatures of positive selection with a self-supervised transformer

Understanding natural selection can help shed light on the genetics underpinning adaptive evolution. The widespread availability of large-scale human ...

Phenotypic reversion and target prioritization for cellular inflammation via representation learning with foundation models

The identification of genetic perturbations that can reverse disease-associated cellular phenotypes toward a healthy state is a central challenge in e...

Developmental and genetic modulation of evidence integration dynamics in zebrafish sensorimotor decision-making

Animals integrate information over time and maintain persistent internal representations of cues to guide decision-making. How the underlying behavior...

Extending 2D foundational DINOv3 representations to 3D segmentation of neonatal brain MR images

Precise volumetric delineation of hippocampal structures is essential for quantifying neurodevelopmental trajectories in pre-term and term infants, wh...