COVID-19

Model selection in hybrid quantum neural networks with applications to quantum transformer architectures

Quantum machine learning models generally lack principled design guidelines, often requiring full resource-intensive training across numerous choices of encodings, quantum circuit designs and initialization strategies to find effective configuration. To address this challenge, we develope the Quantum Bias-Expressivity Toolbox ($\texttt{QBET}$), a framework for evaluating quantum, classical, and hy...

FluidGaussian: Propagating Simulation-Based Uncertainty Toward Functionally-Intelligent 3D Reconstruction

Real objects that inhabit the physical world follow physical laws and thus behave plausibly during interaction with other physical objects. However, current methods that perform 3D reconstructions of real-world scenes from multi-view 2D images optimize primarily for visual fidelity, i.e., they train with photometric losses and reason about uncertainty in the image or representation space. This app...

Mitigating Objectness Bias and Region-to-Text Misalignment for Open-Vocabulary Panoptic Segmentation

Open-vocabulary panoptic segmentation remains hindered by two coupled issues: (i) mask selection bias, where objectness heads trained on closed vocabu...

From Masks to Pixels and Meaning: A New Taxonomy, Benchmark, and Metrics for VLM Image Tampering

Existing tampering detection benchmarks largely rely on object masks, which severely misalign with the true edit signal: many pixels inside a mask are...

Systematic assessment of machine learning-based variant annotation methods for rare variant association testing

Machine learning-based annotation methods are increasingly used to assess the pathogenicity of genetic variants, but their performance at prioritizing...

Evolutionary and Deep Learning Models Highlight Deleterious Mutations Behind the History of Sugar Beet Breeding

Sugar beet (Beta vulgaris ssp. vulgaris) is a major global source of sucrose. Domestication and a history of breeding have shaped the distribution of ...

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...

Machine Learning Enabled Smartphone CRISPR-Cas12a Lateral Flow Platform for Sensitive Detection of Circulating HPV DNA

Persistent infection with high-risk human papillomavirus (HPV) is the primary cause of cervical cancer and other HPV-related malignancies. Effective s...

The results of Transcriptome-wide Mendelian Randomization (TWMR) in large-scale populations can directly validate, across scales, the results of causal inference from deep learning combined with double machine learning on single-cell transcriptomes of human samples.

Objective Aiming at the core problems prevalent in biomedical research, including the "translational distance", the difficulty in aligning cross-scale...

An AI-Driven Decision-Support Tool for Triage of COVID-19 Patients Using Respiratory Microbiome Data

Accurate clinical triage is critical for optimizing decision-making and resource allocation during infectious disease outbreaks such as COVID-19. In t...

ABAG-Rank: Improving Model Selection of AlphaFold Antibody-Antigen Complexes by Learning to Rank

Motivation: AlphaFold has transformed structural biology with an unprecedented accuracy in modeling protein structures and their interactions with bio...

Rethinking MLLM Itself as a Segmenter with a Single Segmentation Token

Recent segmentation methods leveraging Multi-modal Large Language Models (MLLMs) have shown reliable object-level segmentation and enhanced spatial pe...

LLM-Augmented Computational Phenotyping of Long Covid

Phenotypic characterization is essential for understanding heterogeneity in chronic diseases and for guiding personalized interventions. Long COVID, a...

SpeciefAI: Multi-species mRNA-level Antibody Framework Generation using Transformers

Motivation: Encoding antibodies (Abs) and nanobodies (Nbs) as mRNA enables in vivo production of therapeutic proteins. However, this approach requires...

M2P: Improving Visual Foundation Models with Mask-to-Point Weakly-Supervised Learning for Dense Point Tracking

Tracking Any Point (TAP) has emerged as a fundamental tool for video understanding. Current approaches adapt Vision Foundation Models (VFMs) like DINO...

Robust-ComBat: Mitigating Outlier Effects in Diffusion MRI Data Harmonization

Harmonization methods such as ComBat and its variants are widely used to mitigate diffusion MRI (dMRI) site-specific biases. However, ComBat assumes t...

Do Understanding and Generation Fight? A Diagnostic Study of DPO for Unified Multimodal Models

Unified multimodal models share a language model backbone for both understanding and generating images. Can DPO align both capabilities simultaneously...

Pixel-level Counterfactual Contrastive Learning for Medical Image Segmentation

Image segmentation relies on large annotated datasets, which are expensive and slow to produce. Silver-standard (AI-generated) labels are easier to ob...

CoNVict: An Agentic AI System for Copy Number Variation Prioritization in Rare Disease Diagnosis

Copy number variants (CNVs) are established contributors to rare genetic disorders, yet their clinical interpretation remains challenging in diagnosti...

Single-Pass Discrete Diffusion Predicts High-Affinity Peptide Binders at >1,000 Sequences per Second across 150 Receptor Targets

De novo peptide design methods traditionally couple generation to 3D structure prediction, limiting throughput to seconds or hours per candidate. Here...