Leukemia

CellFluxRL: Biologically-Constrained Virtual Cell Modeling via Reinforcement Learning

Building virtual cells with generative models to simulate cellular behavior in silico is emerging as a promising paradigm for accelerating drug discovery. However, prior image-based generative approaches can produce implausible cell images that violate basic physical and biological constraints. To address this, we propose to post-train virtual cell models with reinforcement learning (RL), leveragi...

CFCML: A Coarse-to-Fine Crossmodal Learning Framework For Disease Diagnosis Using Multimodal Images and Tabular Data

In clinical practice, crossmodal information including medical images and tabular data is essential for disease diagnosis. There exists a significant modality gap between these data types, which obstructs advancements in crossmodal diagnostic accuracy. Most existing crossmodal learning (CML) methods primarily focus on exploring relationships among high-level encoder outputs, leading to the neglect...

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...

Cell-Type Prototype-Informed Neural Network for Gene Expression Estimation from Pathology Images

Estimating slide- and patch-level gene expression profiles from pathology images enables rapid and low-cost molecular analysis with broad clinical imp...

Revisiting foundation models for cell instance segmentation

Cell segmentation is a fundamental task in microscopy image analysis. Several foundation models for cell segmentation have been introduced, virtually ...

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...

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...

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...

An Interpretable Machine Learning Framework for Non-Small Cell Lung Cancer Drug Response Analysis

Lung cancer is a condition where there is abnormal growth of malignant cells that spread in an uncontrollable fashion in the lungs. Some common treatm...

Understanding Cell Fate Decisions with Temporal Attention

Understanding non-genetic determinants of cell fate is critical for developing and improving cancer therapies, as genetically identical cells can exhi...

HistoAtlas: A Pan-Cancer Morphology Atlas Linking Histomics to Molecular Programs and Clinical Outcomes

We present HistoAtlas, a pan-cancer computational atlas that extracts 38 interpretable histomic features from 6,745 diagnostic H&E slides across 21 TC...

Learning Universal Representations of Intermolecular Interactions with ATOMICA

Molecular interactions underlie nearly all biological processes, but many representation learning models either focus on single entities or are traine...

Personalized Cell Segmentation: Benchmark and Framework for Reference-Guided Cell Type Segmentation

Accurate cell segmentation is critical for biological and medical imaging studies. Although recent deep learning models have advanced this task, most ...

Rational Design of Selective IL-2-based Activators for CAR T Cells Using AlphaFold3 and Physics-Informed Machine Learning

Recombinant human Interleukin-2 (rhIL-2, Aldesleukin) is used in immunotherapy for metastatic melanoma and renal cell carcinoma. Low-dose IL-2 has bee...

DeepHistoViT: An Interpretable Vision Transformer Framework for Histopathological Cancer Classification

Histopathology remains the gold standard for cancer diagnosis because it provides detailed cellular-level assessment of tissue morphology. However, ma...

Identification and Developmental Analysis of the Facial Characteristics Associated with Sickle Cell Disease using Machine Learning

Background Sickle cell disease (SCD) is a common inherited genetic disorder and contributor to global childhood mortality and morbidity. In the Democr...

Experimental multi-center validation of a radiomics-based photonic quantum precision medicine architecture for lesion-level prediction of anti-PD-1 response in non-small cell lung cancer

Background: Previous research has shown that radiomics-based machine learning models are promising precision medicine tools for lesion-level predictio...

Deep Learning Enabled 3D Multi-Omic Analysis Reveals Molecular Signatures of Heterogeneous Response to Chemotherapy in Pancreatic Cancer

Resistance to systemic therapy is a major unmet challenge in pancreatic cancer. To identify potential mechanisms of resistance, we developed a novel 3...

A Resolution-Agnostic Geometric Transformer for Chromosome Modeling Using Inertial Frame

Chromosomes are the carriers of genetic information. Further understanding their 3D structure can help reveal gene-regulatory mechanisms and cellular ...

LLMsFold: Integrating Large Language Models and Biophysical Simulations for De Novo Drug Design

The discovery of novel small molecules is challenging because of the vastness of chemical space and the complexity of protein-ligand interactions, lea...