Oncology/Hematology

ChatSpatial: Schema-Enforced Agentic Orchestration for Reproducible and Cross-Platform Spatial Transcriptomics

Spatial transcriptomics has transformed our ability to study tissue architecture at molecular resolution, yet analyzing these data demands navigating dozens of computational methods across incompatible Python and R ecosystems---forcing researchers to devote more effort to making tools function than to pursuing biological questions. We present ChatSpatial, a platform in which the LLM selects from p...

MAP: A Knowledge-driven Framework for Predicting Single-cell Responses for Unprofiled Drugs

Predicting how cells respond to chemical perturbations is one of the goals for building virtual cells, yet experimentally profiled compounds cover only a small fraction of this space. Existing models struggle to generalize to unprofiled compounds, as they typically treat drugs as isolated identifiers without encoding their mechanistic relationships. We present MAP, a framework that integrates stru...

Uncertainty-aware synthetic lethality prediction with pretrained foundation models

Synthetic lethality (SL) offers a promising paradigm for targeted cancer therapy, yet experimental identification of SL gene pairs remains costly, con...

Onco-Shikshak: An AI-Native Adaptive Learning Ecosystem for Medical Oncology Education

Medical oncology education faces a dual crisis: knowledge velocity that outpaces static curricula and large language model (LLM) risks hallucination a...

Act or Defer: Error-Controlled Decision Policies for Medical Foundation Models

Clinical deployment of foundation models requires decision policies that operate under explicit error budgets, such as a cap on false-positive clinica...

VALIDATION OF PROGRESS, A SIMPLE MACHINE-LEARNING DERIVED RISK STRATIFICATION SCORE FOR CASTRATION-RESISTANT PROSTATE CANCER

Purpose: Castration-resistant prostate cancer (CRPC) is characterized by marked clinical heterogeneity and poor long-term survival, underscoring the n...

Modeling Microbiome Modulation of Tumor Metabolic Networks to Predict Synergistic Therapies

Differences in microbiome composition profoundly influence drug response, yet methods to model the metabolic interplay between tumors, microbes, and t...

Towards Translational Sleep Staging: A Cross-Species Deep-Learning Model for Rodent and Human EEG

Study Objectives Automated sleep staging underpins clinical sleep assessment and translational neuroscience, yet most data analyses work addresses hum...

CellSwarm: LLM-Driven Cell Agents Recapitulate Tumor Microenvironment Dynamics and Sense Indirect Genetic Perturbations

Agent-based models of the tumor microenvironment (TME) traditionally rely on hand-coded rules that cannot generalize beyond their programmed logic. He...

Transforming Histology into Virtual Multiplex Immunofluorescence to Decode Prognostic Spatial Immunity in Hepatocellular Carcinoma

The spatial organization of the tumor immune microenvironment (TIME) drives hepatocellular carcinoma (HCC) prognosis but remains unquantifiable on rou...

A Fast and Practical Column Generation Approach for Identifying Carcinogenic Multi-Hit Gene Combinations

Cancer is often driven by specific combinations of an estimated two to nine gene mutations, known as multi-hit combinations. Identifying these combina...

MM-NeuroOnco: A Multimodal Benchmark and Instruction Dataset for MRI-Based Brain Tumor Diagnosis

Accurate brain tumor diagnosis requires models to not only detect lesions but also generate clinically interpretable reasoning grounded in imaging man...

Enhancing Renal Tumor Malignancy Prediction: Deep Learning with Automatic 3D CT Organ Focused Attention

Accurate prediction of malignancy in renal tumors is crucial for informing clinical decisions and optimizing treatment strategies. However, existing i...

End-to-End PET/CT Interpretation and Quantification with an LLM-Orchestrated AI Agent: A Real-World Pilot Study

Background: Although deep learning models have improved individual PET analysis, image processing and quantification tasks, end-to-end automation from...

Domain-adaptation deep learning models do not outperform simple baseline models in single-cell anti-cancer drug sensitivity prediction

Tumor drug response is profoundly shaped by cellular heterogeneity, making single-cell resolution essential for precision oncology. While drug-respons...

OriGene: A Self-Evolving Virtual Disease Biologist Automating Therapeutic Target Discovery

Therapeutic target discovery remains a critical yet intuition-driven bottleneck in drug development, typically relying on disease biologists to labori...

Virtual Biopsy for Intracranial Tumors Diagnosis on MRI

Deep intracranial tumors situated in eloquent brain regions controlling vital functions present critical diagnostic challenges. Clinical practice has ...

Small Language Models for Privacy-Preserving Clinical Information Extraction in Low-Resource Languages

Extracting clinical information from medical transcripts in low-resource languages remains a significant challenge in healthcare natural language proc...

An Integrated Deep Learning Framework for Small-Sample Biomedical Data Classification: Explainable Graph Neural Networks with Data Augmentation for RNA sequencing Dataset

Applying deep learning models to RNA-Seq data poses substantial challenges, primarily due to the high dimensionality of the data and the limited sampl...

XMorph: Explainable Brain Tumor Analysis Via LLM-Assisted Hybrid Deep Intelligence

Deep learning has significantly advanced automated brain tumor diagnosis, yet clinical adoption remains limited by interpretability and computational ...