Oncology/Hematology

Deep Learning for Molecular and Genomic Characterization of Lung Cancer in Never-Smokers Using Hematoxylin and Eosin-Stained Images

Despite promising results in using deep learning to infer genetic features from histological whole-slide images (WSIs), no prior studies have specifically applied these methods to lung adenocarcinomas from subjects who have never smoked tobacco (NS-LUAD) – a molecularly and histologically distinct subset of lung cancer. Existing models have focused on LUAD from predominantly smoker populations, wi...

MultiAlloDriver: a multi-model method to predict and identify cancer driver mutations

A minority of driver mutations in cancer significantly alter protein structure and key functionalities, thereby driving cancer progression. Consequently, the prediction and identification of driver mutations hold critical implications for targeted cancer therapy. This study introduces MultiAlloDriver, a novel multi-modal machine learning model based on an attention mechanism, which for the first t...

Transcriptional Signatures of Field Cancerization in Gastric Cancer

The high rate of local recurrence in gastric adenocarcinoma (GA) suggests that carcinogenesis is not a focal event but a field-wide process. This phen...

Pocket-based molecule generation with an SE(3)-equivariant language model leads to a potent and selective HPK1 inhibitor with in vivo efficacy

Deep learning shows promise in structure-based drug discovery, yet challenges persist in generating pharmacologically plausible molecules with valid 3...

Translating clinical gene sequencing into a foundational representation of tumor subtype

While gene sequencing is routine in cancer care, translating sequences into treatment decisions remains a challenge. Here we introduce MutationProject...

Interpretable Deep Learning Reveals Biologically Relevant Spatial Gene Expression Patterns in Lung Tumors and their Microenvironment

Lung adenocarcinoma (LUAD), the most common subtype of non–small cell lung cancer (NSCLC) exhibits profound histological and molecular heterogeneity, ...

SpaPheno: Linking Spatial Transcriptomics to Clinical Phenotypes with Interpretable Machine Learning

Linking spatial transcriptomic data to clinically relevant phenotypes is essential for advancing spatially informed precision oncology. Here, we prese...

BLMPred: predicting linear B-cell epitopes using pre-trained protein language models and machine learning

B-cells get activated through interaction with B-cell epitopes, a specific portion of the antigen. Identification of B-cell epitopes is crucial for a ...

scXpand: Pan-cancer detection of T-cell clonal expansion from single-cell RNA sequencing without paired single-cell TCR sequencing

Advances in single-cell sequencing have enabled detailed characterization of T-cell clonal dynamics in cancer. However, analyses aiming to link transc...

An integrative machine learning approach identifies the centrality of ferroptosis, cuproptosis, and immune pathway crosstalk for breast cancer stratification and therapy guidance

Breast cancer (BRCA) is a leading cause of cancer-related mortality in women, characterized by marked heterogeneity in molecular subtypes, immune micr...

Lung Adenocarcinoma Just Desserts: An Expanding Pie of Activating Oncogenes or a Layer Cake of Integrated Alterations

The molecular landscape of lung adenocarcinoma (LUAD) is often summarized as a “pie chart” of driver oncogenes, suggesting identification and targetin...

T-cell receptor specificity landscape revealed through de novo peptide design

T-cells play a key role in adaptive immunity by mounting specific responses against diverse pathogens. An effective binding between T-cell receptors (...

PathPCNet: Pathway Principal Component-Based Interpretable Framework for Drug Sensitivity Prediction

Precision medicine aims to identify significant biomarkers and effective drugs tailored to individual genomic profiles, thereby enabling personalized ...

Multimodal foundation model predicts zero-shot functional perturbations and cell fate dynamics

Deciphering cell type-specific perturbation effects on genes and cellular states demands considerable experimental resources. To overcome this challen...

Differentiation hierarchy in adult B cell acute lymphoblastic leukemia at clonal resolution

While a differentiation hierarchy with leukemia-initiating stem cells (LICs) at the apex is well documented for acute myeloid leukemia, the existence ...

HRDPath: An Explainable Multi-Model Deep Learning Architecture for Predicting Homologous Recombination Deficiency from Histopathology Images

Homologous recombination deficiency (HRD) is a critical biomarker for guiding treatment decisions in high-grade serous tubo-ovarian carcinoma (HGSOC),...

KM-GPT: An Automated Pipeline for Reconstructing Individual Patient Data from Kaplan–Meier Plots

Reconstructing individual patient data (IPD) from Kaplan–Meier (KM) plots provides valuable insights for evidence synthesis in clinical research. Howe...

Mitosis Detection in the Wild Using Detection Transformers

Identification of mitotic cells and its down-stream analysis, is an important parameter in understanding the pathology of cancer, predicting response ...

Advanced Deep Learning Enables Prediction of Allogeneic Stem Cell Mobilization Success

Hematopoietic stem and progenitor cell (HSPC) transplantation offers a potentially curative therapy for aggressive hematologic malignancies and bone m...

Beyond Sequence Similarity: ML-Powered Identification of pHLA Off-Targets for TCR-Mimic Antibodies Using High Throughput Binding Kinetics

T-cell receptor mimic (TCRm) antibodies are an emerging class of tumor-targeting agents used in advanced immunother-apies such as bispecific T-cell en...