Genetics

DIVER:Diving Deeper into Distilled Data via Expressive Semantic Recovery

Dataset distillation aims to synthesize a compact proxy dataset that is unreadable or non-raw from the original dataset for privacy protection and highly efficient learning. However, previous approaches typically adopt a single-stage distillation paradigm, which suffers from learning specific patterns that overfit on a prior architecture, consequently suppressing the expression of semantics and le...

Swarm-GestaltMatcher: distributed Gestalt learning through Swarm Learning to enhance facial phenotyping for rare genetic syndromes

Deep learning-based facial phenotyping represents a major paradigm shift in the diagnosis of rare and ultra-rare genetic disorders. By capturing disease-specific craniofacial 'gestalts' that are often subtle, overlapping, but overlooked in routine clinical practice, these technologies surpass the traditional limits of dysmorphology assessment. Despite this, data scarcity and stringent privacy poli...

Cyst-type epithelial heterogeneity shapes therapeutic responsiveness in ADPKD

Autosomal dominant polycystic kidney disease (ADPKD) exhibits substantial interpatient variability in disease course and therapeutic response, but the...

Corpus-wide causality: Algorithm design & application for aggregating gene-disease causal evidence

Identifying causal relationships, rather than mere associations, is essential for applications such as finding genes driving diseases and guiding drug...

Unified sampling framework and experimental benchmarking of sequence- and structure-based protein models

Generative models are increasingly used for protein design, but the lack of standardized evaluation frameworks limits comparison across model classes ...

Detecting and quantifying overparametrization in RNA language models with REDIAL

While RNA language models (LMs) have served as foundation models (FMs) to advanced structural prediction, their evaluation relies heavily on supervise...

Transferable Transcriptional Topic Modeling Traces Medulloblastoma Subtypes to Distinct Cerebellar Developmental States

Single-cell transcriptomics transformed our understanding of cellular heterogeneity, yet cross-dataset comparison remains fundamentally limited by bat...

Virtual screening and zebrafish phenotype-based evaluation argues against repurposing 4-phenylbutyrate for STXBP1-relateddisorders

Syntaxin-binding protein 1 (STXBP1) mutations lead to severe epilepsy, intellectual disability, developmental delay, and movement disorder. Effective ...

Machine Learning Analysis to Define Cell Lineage in Leiomyosarcoma

Introduction Cellular differentiation and lineage commitment are known to be associated with differences in DNA methylation. Leiomyosarcoma (LMS) is a...

Integrative Genomic, Single-Cell, and Functional Profiling of the CD48-CD244 Axis and NK-Cell Dysfunction in Multiple Myeloma

Multiple myeloma (MM) orchestrates immune evasion by subverting natural killer (NK) cell function. CD48, one of the most abundant NK-ligands on MM cel...

pyTrance finds co-localizing RNAs in subcellular spatial transcriptomics data

Regulation of RNA subcellular localization is crucial for cellular functions in health and disease. For example, local translation of co-localized RNA...

FiberLM: A Transformer-Based Model for Mouse Brain Diffusion MRI Tractography Guided by Viral Tracer Data

Diffusion MRI (dMRI) tractography provides a non-invasive method for mapping whole-brain structural connectivity. However, its application is limited ...

Complex-Valued Phase-Coherent Transformer

Complex-valued Transformers have largely inherited softmax attention from real-valued architectures. However, row-normalised token competition is not ...

AssayBench: An Assay-Level Virtual Cell Benchmark for LLMs and Agents

Recent advances in machine learning and large-scale biological data collections have revived the prospect of building a virtual cell, a computational ...

MISP-Bench: Decomposing User-Provided False Priors into Answer, Rationale, and Guard Effects

Large language models in clinical and educational settings routinely receive user-provided context containing incorrect prior beliefs. Existing benchm...

Interpretable neural networks prioritize cancer driver genes from genome-wide dependency landscapes

Identifying cancer driver genes and their therapeutic impact remains a core challenge in computational cancer biology. We introduce xNNDriver and xAED...

Integrative taxonomy using traits and genomic data for Species Delimitation with Deep learning

Recognizing species boundaries in complex speciation scenarios, including those involving gene flow and demographic fluctuations, remains a challenge,...

Entropy Sorting Feature Selection: information-theoretic gene set identification improves single-cell RNA sequencing data interpretability

Single-cell RNA sequencing (scRNA-seq) has transformed our ability to resolve cellular heterogeneity, but extracting meaningful signals remains challe...

UPhAIR: A Hybrid Pipeline for Generating Understandable Post-hoc AI Reports in Glioma IDH Mutation Status Prediction

Clinical adoption of machine learning (ML) in medical imaging is limited by the lack of interpretability. To address this, we present understandable p...

An Explainable Deep Learning Framework for Imaging Genetics: Deriving Brain-Genotype Scores From MRI to Link Genetic Variation, Brain Structure, and Cognition

Imaging genetics aims to understand how genetic variation influences brain structure and cognitive function. Traditional approaches often rely on imag...