Oncology/Hematology

A Hybrid PINN-DE Framework for Data-Driven Parameter Estimation of Tumor-Immune Dynamics in Bladder Cancer

Bladder cancer presents significant clinical challenges due to its complex immune microenvironment and highly heterogeneous response to treatments. To create accurate, individualized models of disease progression, we first construct a system of Ordinary Differential Equations (ODEs) that captures tumor-immune interactions. We address the challenge of estimating unknown parameters by performing a r...

Systematic Evaluation of Transfer Learning Strategies for Clinical Chemotherapy Response Prediction

Accurately predicting chemotherapy response remains a major challenge in precision oncology. Although machine-learning models based on tumour omics data have shown promise, the majority of existing studies are trained and evaluated on pre-clinical cell-line datasets, leaving their clinical applicability insufficiently characterised. In this study, we systematically evaluate a range of transfer-lea...

A Pan-Cancer Ex Vivo Drug Screen Atlas for Functional Precision Oncology

Compared to immortalized cell lines, patient-derived organoids and other ex vivo models have been shown to better recapitulate patient responses to th...

Fast-cWDM Brain MRI: Fast Conditional Wavelet Diffusion Model for Synthesis Brain MRI Modality

In this paper, we present a novel and efficient framework for cross-modality medical image synthesis, developed for BraSyn-Task 8. Our method combines...

Context-aware Skin Cancer Epithelial Cell Classification with Scalable Graph Transformers

Whole-slide images (WSIs) from cancer patients contain rich information that can be used for medical diagnosis or to follow treatment progress. To aut...

Biomarker Identification in Pancreatic Cancer Through Concordant Differential Expression and Interpretable Machine Learning Analyses

Background: Pancreatic ductal adenocarcinoma is one of the most aggressive and lethal malignancies of the gastrointestinal tract. The poor prognosis i...

Prototype Instance-semantic Disentanglement with Low-rank Regularized Subspace Clustering for WSIs Explainable Recognition

The tumor region plays a key role in pathological diagnosis. Tumor tissues are highly similar to precancerous lesions and non tumor instances often gr...

Analysis of Age-Specific Dysregulation of miRNAs in Lung Cancer Via Machine learning: Biomarker Identification and Therapeutic Implications in Patients Aged 60 and Above.

Lung cancer is the leading cause of cancer-related mortality worldwide, predominantly affects older individuals, with non-small cell lung cancer (NSCL...

evoCancerGPT: Generating Zero-Shot Single-Cell and Single-Sample Cancer Progression Through Transfer Learning

Cancer evolution is driven by complex changes in gene expression as cells transition and change states during tumorigenesis. Single-cell RNA sequencin...

Deep-Learning Atlas Registration for Melanoma Brain Metastases: Preserving Pathology While Enabling Cohort-Level Analyses

Melanoma brain metastases (MBM) are common and spatially heterogeneous lesions, complicating cohort-level analyses due to anatomical variability and d...

A single-cell atlas and aging clock define biological age and risk-associated stem cell states in human hematopoiesis

Aging of hematopoietic stem and progenitor cells (HSPCs) impairs regenerative capacity and predisposes to hematological diseases. Here, we constructed...

UCSF RMaC: University of California San Francisco 3D Multi-Phase Renal Mass CT Dataset with Tumor Segmentations

Current standard of care imaging practices cannot reliably differentiate among certain renal tumors such as benign oncocytoma and clear cell renal cel...

A radiation-free screening system for adolescent idiopathic scoliosis using deep learning on 3D back surface point clouds

Widespread screening for Adolescent Idiopathic Scoliosis (AIS) is critical for timely intervention but is currently constrained by the radiation risks...

Generative AI Guided Design of High-Affinity T cell Receptors

Developing T cell receptors (TCRs) with sufficiently high affinity for tumor antigens (TAs) remains a fundamental challenge in TCR-T immunotherapy. Ex...

Genetic variation shapes human mRNA translation and disease risk

Genetic variation can influence protein abundance through translation, yet this regulatory layer remains poorly defined. We developed a deep learning ...

Deep learning-based non-invasive profiling of tumor transcriptomes from cell-free DNA for precision oncology

Circulating tumor DNA (ctDNA) profiling from liquid biopsies is increasingly adopted as a minimally invasive solution for clinical cancer diagnostic a...

Cellular Aging Signatures in the Plasma Proteome Record Human Health and Disease

Aging is asynchronous across cells and organs, but whether plasma proteins can capture cell type-specific aging and predict disease and mortality rema...

Learning Glioblastoma Tumor Heterogeneity Using Brain Inspired Topological Neural Networks

Accurate prognosis for Glioblastoma (GBM) using deep learning (DL) is hindered by extreme spatial and structural heterogeneity. Moreover, inconsistent...

X-SPATIO: An Explanatory Deep Learning Pipeline for the Prediction and Visualization of Spatially Resolved Biomarker Expression in Triple-Negative Breast Cancer

Histopathologic evaluation remains central to cancer diagnosis and treatment planning, yet the molecular programs underlying distinct tissue morpholog...

Network-based integration of gene expression and DNA methylation identifies prognostic biomarkers for early-stage pancreatic cancer

Pancreatic ductal adenocarcinoma remains one of the most lethal malignancies, largely due to the absence of reliable early stage biomarkers. Here, we ...