Leukemia

Retrieval-Augmented Vision Foundation Models for Robust Leukemia Cell Classification across Multiple Microscopy Datasets

Leukemia cell image classification is challenged by real-world domain shifts from acquisition, staining, illumination, and site protocols, causing single-dataset models to generalize poorly in real clinical scenarios. This work presents a robust framework for leukemia classification across multiple heterogeneous datasets using a two-stage pipeline with a pretrained vision foundation model. Stage 1...

AI-Driven Computational Design of Peptide-Based WWP1 Inhibitors as Promising Therapeutic Agents Against Breast Cancer, Including Triple-Negative Subtype

Breast cancer (BC) is the second most common noncutaneous cancer and the second leading cause of cancer-related death in women. BC is classified into three primary subtypes, with triple-negative breast cancer (TNBC) having the poorest prognosis because it lacks specific targetable markers. Preclinical studies on TNBC indicated a common occurrence of diminished tumor-suppressor activity of PTEN, ac...

Benchmarking single-cell foundation models in a zero-shot setting

Single-cell foundation models have recently emerged as a promising approach for learning general-purpose representations from large-scale transcriptom...

Explainable HGT-based framework for predicting human dark kinase protein-pathway associations by leveraging BERT-based embeddings and WGAN-GP

Discovery of pathway associations and druggability can leverage underutililized dark kinase genes for treating complex diseases (proven for cancer and...

Paired Recipient-based Evaluation of Survival Prediction for Deceased Donor Kidney Transplants

There has been significant interest in using machine learning algorithms to predict kidney transplant outcomes, such as the number of years until a gr...

Deep-learning based 3D segmentation of heterogeneous lizard claw tissue from CT data

The accurate segmentation of lizard claws is important as they are materially heterogeneous, comprising both bone and keratinous tissue. This study pr...

scINTILLA: Single-Cell Integrated Inference, Labelling, and Landscape Analysis for Cell-Type Annotation Quality Assessment

Single-cell RNA sequencing has enabled the construction of comprehensive cell atlases, yet the quality and coherence of the cell-type annotations with...

Simulation-Trained Deep Learning for Automated Cell-Based HLA Antibody Assay Interpretation in Pre-Transplant Diagnostics

Preformed and de novo antibodies against donor human leukocyte antigen (HLA) antigens remain a major cause of antibody-mediated rejection and graft lo...

Infrared Imaging Empowered by Artificial Intelligence for Pediatric Skeletal Triage: A Narrative Review and Future Perspectives

Background. Pediatric musculoskeletal trauma represents up to 18% of pediatric ED visits, yet diagnosis still depends on ionizing radiography. Cumulat...

Comparison of nuisance function construction strategies for double machine learning causal inference in single-cell transcriptomics: shared unsupervised deep learning does not require cross-fitting

Inferring "whether a change in the expression of a given gene causally affects the disease state" from observational single-cell transcriptomic data i...

Single-cell foundation models predict durable CAR T response despite imperfect cell annotation

CD19 targeted chimeric antigen receptor (CAR) T cell therapy achieves high initial response rates in B cell acute lymphoblastic leukemia (B ALL), yet ...

Mapping the Transcriptional Landscape of Drug Responses in Primary Human Cells Using High-Throughput DRUG-seq

To advance our understanding of drug action in physiologically-relevant systems, we developed a high-throughput transcriptomic atlas of compound respo...

In Silico Trial Simulation with Artificial Intelligence-Generated Synthetic Control Cohorts Reproduces Results of a Randomized Controlled Trial in Acute Myeloid Leukemia

Rising costs, slow accrual and molecular substratification of cancers necessitate novel clinical trial designs. We demonstrate that artificial intelli...

Lesioned Multimodal Language Models Reproduce Aphasic Picture-Naming Patterns

Aphasia following stroke commonly produces systematic naming errors with characteristic profiles, but whether general-purpose language models not desi...

OCellus: A Language-Model Framework for Single-Cell, Spatial, and Perturbation Biology with Natural-Language Reasoning

Computational modeling of cellular behavior - the virtual cell - has emerged as a stated grand challenge at the intersection of artificial intelligenc...

Automated Phenotypic Characterization in Rare Hematologic Malignancies Using a Large Language Model-Based Framework

Background. Diagnosis and risk stratification in rare hematologic malignancies such as myeloproliferative neoplasms (MPNs) - polycythemia vera (PV), e...

Deep dynamical models of single-cell multiomic velocities predict loss-of-function and rescue perturbations in B cells

We present DynaVelo, a generative neural ordinary differential equation model that learns the joint dynamics of gene expression and transcription fact...

Residual Multi-Modal Learning for Pan-Breast-Cancer Drug Response Prediction

Predicting drug sensitivity across diverse cancer cell lines remains a fundamental challenge in precision oncology, particularly for data-scarce cell ...

Multi-Timepoint Risk Stratification in Rare Cancers: A Computational Framework Validated against Published Ewing Sarcoma Trial Data

Three audiences -- the family of a newly diagnosed Ewing sarcoma patient, the long-term survivor, and the cooperative-group trial statistician -- rece...

Multimodal profiling for prediction of primary resistance to anti-PD-(L)1 therapy in advanced non-small-cell lung cancer: the prospective PIONeeR biomarkers study

Background Pretreatment prediction of primary resistance to anti-PD-(L)1 therapy in advanced non-small-cell lung cancer (NSCLC) remains an unmet clini...