Genetics

GeneBench: Assessing AI Agents for Multi-Stage Inference Problems in Genomics and Quantitative Biology

We introduce GeneBench, a benchmark for AI agents on realistic multi-stage scientific data analysis in genetics and quantitative biology. Existing biology benchmarks mostly measure knowledge retrieval, execution of routine pipelines, or a single analysis step. Yet they do not capture the broader scope of work that occupies much of computational scientists' time: cleaning and normalizing assay, phe...

Entrenchment of germline amino-acid differences in antibody affinity maturation

Entrenchment - epistasis that locks in amino acid differences between homologous proteins, so each disfavors substitutions toward the other's state - has been demonstrated along individual protein lineages over deep evolutionary time. Antibodies offer a unique system for studying entrenchment: multiple homologous germline V gene paralogs provide diverse starting points, and the rapid somatic evolu...

Evaluating Post-hoc Explanations of the Transformer-based Genome Language Model DNABERT-2

Explaining deep neural network predictions on genome sequences enables biological insight and hypothesis generation-often of greater interest than pre...

Optimizing Diffusion Priors with a Single Observation

While diffusion priors generate high-quality posterior samples across many inverse problems, they are often trained on limited training sets or purely...

Causal-Transformer with Adaptive Mutation-Locking for Early Prediction of Acute Kidney Injury

Accurate early prediction of Acute Kidney Injury (AKI) is critical for timely clinical intervention. However, existing deep learning models struggle w...

X-PCR: A Benchmark for Cross-modality Progressive Clinical Reasoning in Ophthalmic Diagnosis

Despite significant progress in Multi-modal Large Language Models (MLLMs), their clinical reasoning capacity for multi-modal diagnosis remains largely...

DistortBench: Benchmarking Vision Language Models on Image Distortion Identification

Vision-language models (VLMs) are increasingly used in settings where sensitivity to low-level image degradations matters, including content moderatio...

Target Parameterization in Diffusion Models for Nonlinear Spatiotemporal System Identification

Machine learning is becoming increasingly important for nonlinear system identification, including dynamical systems with spatially distributed output...

Enabling the prediction of phage receptor specificity from genome data

Predicting which receptor a phage binds to from genome sequence alone has remained an intractable challenge, principally because the experimental phen...

A Conversational Artificial Intelligence Framework for Comparative Pathway-Level Profiling of Sezary Syndrome and Primary Cutaneous CD8+ Aggressive Epidermotropic Cytotoxic T-Cell Lymphoma (PCAECTCL)

Background: Sezary syndrome (SS) is an aggressive leukemic variant of cutaneous T-cell lymphoma (CTCL) with distinct clinical and biological features ...

Vector2Variant: Discovery of Genetic Associations from ML Derived Representations without Phenotype Engineering

Genome-wide association studies (GWAS) have transformed our understanding of human biology, but are constrained by the need for predefined phenotypes....

Generative Modeling of Complex-Valued Brain MRI Data

Objective. Standard Magnetic Resonance Imaging (MRI) reconstruction pipelines discard phase information captured during acquisition, despite evidence ...

U-Probe: universal agentic probe design for imaging-based spatial-omics

Probe design for fluorescence in situ hybridization (FISH) underpins spatial transcriptomics, three-dimensional genome studies, and clinical diagnosti...

EVEE: Interpretable variant effect prediction from genomic foundation model embeddings

Predicting the clinical significance of genetic variants remains a central challenge in genomic medicine, with most observed variants classified as va...

From Pixels to Nucleotides: End-to-End Token-Based Video Compression for DNA Storage

DNA-based storage has emerged as a promising approach to the global data crisis, offering molecular-scale density and millennial-scale stability at lo...

TFBindFormer: A Cross-Attention Transformer for Transcription Factor-DNA Binding Prediction

Transcription factors (TFs) are central regulators of gene expression, and their selective recognition of genomic DNA underlies various biological pro...

Integrating computational chemistry and machine learning to predict KRAS mutation-induced resistance

Mutation-induced drug resistance is a major contributor to the failure of targeted cancer therapies, particularly in tumors driven by mutations in the...

Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution

Latent diffusion models for medical image super-resolution universally inherit variational autoencoders designed for natural photographs. We show that...

T2I-BiasBench: A Multi-Metric Framework for Auditing Demographic and Cultural Bias in Text-to-Image Models

Text-to-image (T2I) generative models achieve impressive visual fidelity but inherit and amplify demographic imbalances and cultural biases embedded i...

Interpretable DNA Sequence Classification via Dynamic Feature Generation in Decision Trees

The analysis of DNA sequences has become critical in numerous fields, from evolutionary biology to understanding gene regulation and disease mechanism...