Genetics

Integrated single-cell and bulk transcriptomic analysis leverages liver metastasis-related genes to develop a prognostic model for colorectal cancer patients

Based on single-cell RNA sequencing data, differentially expressed genes (LMR DEGs) between colorectal cancer liver metastasis epithelium and primary colorectal cancer epithelium show potential as novel biomarkers for colorectal cancer prognosis. This study first utilized single-cell RNA sequencing data to characterize the cellular landscape of primary colorectal cancer and liver metastasis, ident...

Contrastive Conformal Sets

Contrastive learning produces coherent semantic feature embeddings by encouraging positive samples to cluster closely while separating negative samples. However, existing contrastive learning methods lack principled guarantees on coverage within the semantic feature space. We extend conformal prediction to this setting by introducing minimum-volume covering sets equipped with learnable generalized...

MEIsensor: a deep-learning method for mobile element insertion discovery

Mobile element insertions (MEIs) are a critical source of structural variation in the human genome, yet their accurate detection remains challenging, ...

TOGGLE delineates fate and function within individual cell types via single cell transcriptomics

Cells that appear transcriptionally identical can maintain vastly different functions or fate, an enduring blind spot in single-cell transcriptomics. ...

Incorporating contextual information into KGWAS for interpretable GWAS discovery

Genome-Wide Association Studies (GWAS) identify associations between genetic variants and disease; however, moving beyond associations to causal mecha...

LAMBDA: A Prophage Detection Benchmark for Genomic Language Models

Transformer-based genomic sequence models represent an emerging frontier in computational biology. Yet, their embeddings have not yet shown the same l...

Predicting Unseen Gene Perturbation Response Using Graph Neural Networks with Biological Priors

Predicting transcriptional responses to genetic perturbations is a central challenge in functional genomics. CRISPR Perturb-seq experiments measure ge...

Scaling and Generalization of Discrete Diffusion Models for Tumor Phylogenies

Tumor phylogenies - rooted trees encoding clonal ancestry and mutation acquisition - are central to understanding cancer evolution, yet generating rea...

FoundedPBI: Using Genomic Foundation Models to predict Phage-Bacterium Interactions

The scalability of phage therapy as a viable alternative or complement to antibiotics is limited by the labor-intensive experimental screening require...

Utility of 3D Facial Analysis As A Biomarker In Rare Diseases Exploration with Hereditary Angioedema

Importance: People living with rare diseases (PLWRD) often face significant challenges in receiving timely and accurate diagnoses, leading to what is ...

PMT: Plain Mask Transformer for Image and Video Segmentation with Frozen Vision Encoders

Vision Foundation Models (VFMs) pre-trained at scale enable a single frozen encoder to serve multiple downstream tasks simultaneously. Recent VFM-base...

An Integrative Genome-Scale Metabolic Modeling and Machine Learning Framework for Predicting and Optimizing Biofuel-Relevant Biomass Production in Saccharomyces cerevisiae

Saccharomyces cerevisiae is a cornerstone organism in industrial biotechnology, valued for its genetic tractability and robust fermentative capacity. ...

Hierarchy-Guided Multimodal Representation Learning for Taxonomic Inference

Accurate biodiversity identification from large-scale field data is a foundational problem with direct impact on ecology, conservation, and environmen...

A Large-Scale Comparative Analysis of Imputation Methods for Single-Cell RNA Sequencing Data

Single-cell RNA sequencing (scRNA-seq) is inherently affected by sparsity caused by dropout events, in which expressed genes are recorded as zeros due...

Learning relationships in epidemiological data using graph neural networks

When designing control strategies for an infectious disease it is critical to identify the key pathways of transmission. Data on infected hosts - when...

Fully Automated Abstraction of Longitudinal Breast Oncology Records with Off-The-Shelf Large Language Models

Background: Manual chart abstraction is a major bottleneck in clinical research. In oncology, important outcomes such as disease recurrence and the tr...

Tokenization to Transfer: Do Genomic Foundation Models Learn Good Representations?

The success of Large Language Models has inspired the development of Genomic Foundation Models (GFMs) through similar pretraining techniques. However,...

Central Dogma Transformer III: Interpretable AI Across DNA, RNA, and Protein

Biological AI models increasingly predict complex cellular responses, yet their learned representations remain disconnected from the molecular process...

SynLeaF: A Dual-Stage Multimodal Fusion Framework for Synthetic Lethality Prediction Across Pan- and Single-Cancer Contexts

Accurate prediction of synthetic lethality (SL) is important for guiding the development of cancer drugs and therapies. SL prediction faces significan...

Predictive and Seasonal Dynamics of the Human Wastewater Virome

Wastewater-based epidemiology provides a scalable, noninvasive framework for population-level infectious disease monitoring, but traditional assays li...