Genetics

To Retain or to Adapt? Generalizing Continual Learning

The Continual Learning (CL) literature has long been driven by the goal of mitigating catastrophic forgetting. This objective rests on a pervasive, often unstated assumption: that a lifelong learner should approximate the Joint-Task Learning (JTL) solution and retain all previously acquired knowledge. We challenge this retention-centered premise, arguing that in non-stationary environments priorit...

AI-Driven Precision Nutrition for Predicting Iron Deficiency Using Genomics, Polygenic Risk Scores, Dietary Patterns, and Personalized Dietary Recommendations

Background: Iron Deficiency Anemia (IDA) is one of the most prevalent nutritional disorders globally and a leading cause of Disability Adjusted Life Years (DALYs). Conventional diagnostic methods fail to detect deficiencies at an early stage and rarely account for individual genetic5 predisposition. Methods: This study proposes an end-to-end AI-driven precision nutrition pipeline integrating publi...

Clinical Impact, Diagnostic Performance, and Prognostic Implications of Plasma Metagenomic Next-Generation Sequencing in Solid Organ Transplant Recipients

Introduction: Plasma metagenomic next-generation sequencing (mNGS) may detect pathogens in solid organ transplant (SOT) recipients, but optimal patien...

Accelerometry-Derived REM Sleep Behavior Disorder Predicts Future Parkinson's Disease in the UK Biobank

Estimating Parkinson's disease (PD) risk years before diagnosis remains an unmet need. We applied a validated machine learning classifier for REM slee...

KinEMbed: Decoding Kinematics from Electromyography via Cross-Modal Contrastive Learning

Decoding hand kinematics from surface electromyography (EMG) is a core challenge in wearable biosignal processing with clinical relevance for prosthet...

Graph Representation Learning of Longitudinal Medical Imaging Trajectories for Treatment Response Prediction

In patients with breast cancer, pathological complete response (pCR) has been established as a clinically meaningful surrogate marker for long-term ou...

Data-Driven Soft Labeling Scales DNA Read Classification to Whole-Body Cell-Type Deconvolution

Cell-type deconvolution, the task of estimating the proportions of constituent cell types in a heterogeneous biological sample, is a core problem in c...

Geometric Causal Models

Scientists often seek to draw causal inferences from structured data that is not independently and identically distributed, such as spatial data, netw...

Deep Learning for Semen Analysis in Male Infertility: Computer Vision, Multimodal Fusion, and Clinical Translation

Male infertility contributes substantially to the global infertility burden, and sperm analysis remains central to diagnosis, treatment planning, and ...

Causal ASCEND: Scalable Two-tier Causal Discovery on High Dimensional Multi-omics Data

Biological systems exhibit a hierarchical structure, characterised by directed flow from upstream regulators to downstream effects. Although this orde...

Deciphering the Tumor Microenvironment: An Integrated Single-Cell RNA-Seq and AI Framework for Novel Biomarker and Therapeutic Target Discovery in Melanoma

Background: Melanoma represents a highly immunogenic and therapeutically challenging malignancy. The complex cellular ecosystem of the tumor microenvi...

Task-adapted biological foundation models uncover perturbation-centric representations

Foundation models have emerged as powerful tools for learning transferable representations of biological systems, yet their latent spaces are typicall...

Integrative analysis of cfDNA features from ultra-low coverage whole genome sequencing enables robust detection of ovarian cancer

Background: Despite advances in circulating tumor DNA analysis, reliable detection of oncological disease from ultra-low coverage whole genome sequenc...

Accurate, comprehensive gene annotation and ortholog identification across thousands of vertebrate genomes with TOGA2

Inferring orthologs and annotating coding genes remain central challenges in genomics, evident by the growing gap between assembled and annotated geno...

Benchmarking the translational potential of AI-based drug-resistance prediction from Mycobacterium tuberculosis whole-genome sequencing data

Background: Tuberculosis, especially drug-resistant tuberculosis (DR-TB) including multidrug-resistant (MDR) and extensively drug-resistant (XDR) stra...

Evo 2's Perception of Single Nucleotide Substitutions in the Genes of Two Plant Model Organisms

Although DNA Large Language Models (DNA-LLMs) offer a path to decoding genetic complexity, our ability to evaluate these models is constrained by our ...

WattmaMod enables high-resolution and extensible RNA modification profiling for nanopore direct RNA sequencing

Nanopore direct RNA sequencing enables direct profiling of RNA modifications on native transcripts, but accurate multi-modification detection remains ...

Models trained with noisy genomes extend bacterial phenotype prediction into deep time

Predicting phenotype from genotype in extant organisms is increasingly tractable through the accumulation of genome sequences and the development of m...

Knowledge-guided Bayesian optimization using pre-trained LLMs speeds up the identification of superior genotypes from germplasm collection

Background: Germplasm collections contain wide genetic diversity that is valuable for plant breeding, but conducting phenotypic evaluation for all gen...

Frequent, context-dependent effects of human genetic variation on Cas9 activity revealed by population-scale GUIDE-seq-2 and deep combinatorial CHANCE-seq profiling

Genome editing enzymes can introduce targeted changes to the DNA in living cells, transforming biological research and enabling the first approved gen...