Oncology/Hematology

Mapping spatial cell-cell communication programs by tailoring chains of cells for transformer neural networks

Recent advances in spatial transcriptomics and computational modeling enable the study of cellular interactions in situ. However, existing methods quantify ligand-receptor activity pairwise or between predefined cell groups, yielding overlapping signals and limited ability to summarize concurrent interactions into programs while localizing communication hotspots. We introduce scCChain, a transform...

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 drug discovery. Predicting how genetic and pathway perturbations reshape gene expression across diverse cellular contexts is a prerequisite for building generalizable cellular foundation models. However, current methods typically fail to extrapolate...

TuLaBM: Tumor-Biased Latent Bridge Matching for Contrast-Enhanced MRI Synthesis

Contrast-enhanced magnetic resonance imaging (CE-MRI) plays a crucial role in brain tumor assessment; however, its acquisition requires gadolinium-bas...

Near-Equivalent Q-learning Policies for Dynamic Treatment Regimes

Precision medicine aims to tailor therapeutic decisions to individual patient characteristics. This objective is commonly formalized through dynamic t...

Gastric-X: A Multimodal Multi-Phase Benchmark Dataset for Advancing Vision-Language Models in Gastric Cancer Analysis

Recent vision-language models (VLMs) have shown strong generalization and multimodal reasoning abilities in natural domains. However, their applicatio...

Spatio-temporal mapping of immune cell dynamics during human sequential lymph node metastasis

Regional lymph node (LN) metastasis critically influences distant metastatic progression, anti-tumour immunity, and patient prognosis. While tumour-in...

Translating Histopathology Foundation Model Embeddings into Cellular and Molecular Features for Clinical Studies

AI-powered pathology foundation models provide general-purpose representations of histopathological images by encoding image tiles into numerical embe...

Cross-Modal Training Using Xenium Spatial Transcriptomics Enables DINO-DETR Based Detection of Vascular Niches in H&E Whole-Slide Images

Background: Tumor vasculature is a key driver of glioma progression, yet routine quantification depends on subjective histopathologic assessment or re...

CytoSyn: a Foundation Diffusion Model for Histopathology -- Tech Report

Computational pathology has made significant progress in recent years, fueling advances in both fundamental disease understanding and clinically ready...

OpenScientist: evaluating an open agentic AI co-scientist to accelerate biomedical discovery

Background: Advances in medicine depend on analyzing large and complex data sources, but discovery is partly constrained by the limited time and domai...

Metastable Neural Assemblies on a Wiring-Weight Continuum

Neural population activity typically evolves on low-dimensional manifolds and can be described as trajectories in attractor-like state spaces, includi...

SpatialFusion: A lightweight multimodal foundation model for pathway-informed spatial niche mapping

Foundation models enable knowledge transfer across data modalities and tasks, yet foundation models for spatial biology remain in their early stages, ...

Unpaired TCRα + TCRβ sequencing is sufficient for training machine learning TCR-epitope recognition predictors

T-cell recognition of infected and malignant cells is elicited by the binding of heterodimeric T-Cell Receptors (TCRs) to epitopes and both the TCR an...

LoGSAM: Parameter-Efficient Cross-Modal Grounding for MRI Segmentation

Precise localization and delineation of brain tumors using Magnetic Resonance Imaging (MRI) are essential for planning therapy and guiding surgical de...

Topology-Guided Biomechanical Profiling: A White-Box Framework for Opportunistic Screening of Spinal Instability on Routine CT

Routine oncologic computed tomography (CT) presents an ideal opportunity for screening spinal instability, yet prophylactic stabilization windows are ...

Synergizing Deep Learning and Biological Heuristics for Extreme Long-Tail White Blood Cell Classification

Automated white blood cell (WBC) classification is essential for leukemia screening but remains challenged by extreme class imbalance, long-tail distr...

A network-based deep learning model integrating subclonal architecture for therapy response prediction in cancer

Predicting treatment response remains challenging in oncology, particularly given the growing diversity of therapeutic options. Despite efforts using ...

Spatial Agent-Based Modeling and Interpretable Machine Learning Predict Combination Therapy Response in HER2-Heterogeneous Breast Cancer

HER2 heterogeneity and reversible phenotypic plasticity play a central role in breast cancer progression and therapeutic resistance, yet how their int...

PathGLS: Evaluating Pathology Vision-Language Models without Ground Truth through Multi-Dimensional Consistency

Vision-Language Models (VLMs) offer significant potential in computational pathology by enabling interpretable image analysis, automated reporting, an...

Sample-Efficient Adaptation of Drug-Response Models to Patient Tumors under Strong Biological Domain Shift

Predicting drug response in patients from preclinical data remains a major challenge in precision oncology due to the substantial biological gap betwe...