Breast Cancer

Development of an EMT-related exosomal miRNA signature that can predict prognosis in hepatocellular carcinoma

Chemoresistance and epithelial-mesenchymal transition (EMT) are associated with failure of cancer chemotherapy and poor survival of patients. We have previously shown that chemoresistance and stemness in hepatocellular carcinoma (HCC) cells was accompanied by the development of partial EMT (p-EMT) and identified a number of EMT-associated proteins that are released from exosomes. In this study, we...

Pathologist-interpretable breast cancer subtyping and stratification from AI-inferred nuclear features

Artificial intelligence (AI) is making notable advances in digital pathology but faces challenges in human interpretability. Here we introduce EXPAND (EXplainable Pathologist Aligned Nuclear Discriminator), the first pathologist-interpretable AI model to predict breast cancer tumor subtypes and patient survival. EXPAND focuses on a core set of 12 nuclear pathologist-interpretable features (NPIFs),...

Artificial intelligence-enabled automated analysis of transmission electron micrographs to evaluate chemotherapy impact on mitochondrial morphology in triple negative breast cancer

Advancements in transmission electron microscopy (TEM) have enabled in-depth studies of biological specimens, offering new avenues to large-scale imag...

Breast Cancer Subtyping with HyperCLSA: A Hypergraph Contrastive Learning Pipeline for Multi-Omics Data Integration

Accurate molecular subtyping of cancer is crucial for advancing personalized medicine. Although multiomics data contain valuable predictive informatio...

DREAMER-S: Deep leaRning-Enabled Attention-based Multiple-instance approaches with Explainable Representations for Spatial biology

Identifying image features that associate strongly with diagnostic or prognostic classes in large-scale, multi-channel spatial imaging is challenging ...

A Machine Learning Model Optimized for Local Data Stratifies Patients for the Adoptive Cell Therapy with Tumor Infiltrating Lymphocytes in Bladder Tumors

Adoptive cell therapy with tumor-infiltrating lymphocytes (ACT-TILs) involves autologous TILs that are expanded ex vivo and then reinfused into the pa...

Transforming Esogastric Cancer Surgery Integrating SpiderMass Mass Spectrometry with Clinical and Microbiome Data for Margin Delineation and Prognosis

Esophageal-gastric cancers (EC) represent a significant global health concern, with esophageal cancer ranking seventh in terms of incidence and mortal...

Machine Learning Ensemble Reveals Age-Specific Responses of Murine Mammary Tissue to Spaceflight With Relevance to Breast Cancer: An Observational Study

Spaceflight presents unique environmental stressors, such as microgravity and radiation, that significantly affect biological systems at the molecular...

Predicting MammaPrint Recurrence Risk from Breast Cancer Pathological Images Using a Weakly Supervised Transformer

Recurrence related to poor prognosis is a leading cause of mortality in patients with breast cancer (BC). The MammaPrint (MP) genomic assay is designe...

Identifying tissue states by spatial protein patterns related to chemotherapy response in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is an aggressive malignancy with limited targeted therapies and variable responses to conventional chemotherapy, ...

Single-cell RNA sequencing and large-scale bulk combination with machine learning reveal gastric cancer-related macrophage heterogeneity

The tumor microenvironment (TME) significantly impacts cancer progression and overall patient survival. However, the complexity of tumor cell-TME inte...

Deep Learning Bridges Histology and Transcriptomics to Predict Molecular Subtypes and Outcomes in Muscle-Invasive Bladder Cancer

Muscle-Invasive Bladder Cancer (MIBC) is a heterogeneous disease with distinct molecular subtypes influencing prognosis and therapeutic response. Howe...

USP-ddG: A Unified Structural Paradigm with Data Efficacy and Mixture-of-Experts for Predicting Mutational Effects on Protein-Protein Interactions

Accurately estimating changes in binding free energy (ΔΔG) is essential for understanding protein-protein interactions (PPIs) and guiding rational pro...

A machine learning framework for supervised treatment response prediction from tumor transcriptomics: A large-scale pan-cancer study

Precision oncology aims to guide treatment decisions using biomarkers. While DNA-based panels are increasingly applied, RNA transcriptomics remain und...

Integrative spatial multi-omics reveals prognostic tumor niches in female genital tumors

Female genital tumors (FGTs), including ovarian, endometrial, and cervical cancers, pose a major global health challenge, yet their spatial and molecu...

Lab-in-the-loop therapeutic antibody design with deep learning

Therapeutic antibody design is a complex multi-property optimization problem with substantial promise for improvement with the application of machine-...

Multiple instance learning on tile level-pathology images provides accurate and interpretable classification for breast cancer molecular subtypes

Accurate breast cancer molecular subtyping is critical for treatment decisions, yet standard methods such as immunohistochemistry and gene expression ...

Foundation model reveals the shared organization of transcription and topologically associating domains

The three-dimensional organization of chromatin into topologically associating domains (TADs) may impact gene regulation by bringing distant genes int...

From Big Data to Small Scales: Machine Learning Enhances Microclimate Model Predictions

1. Microclimates are critical for understanding how organisms interact with their environments, influencing behaviour, physiology, and species distrib...

Deep learning inference of universal dormancy pseudotime reveals the cellular targets of anti-cancer therapies

Controlled exit from and re-entry into the cell cycle is essential for multi-cellular life, while aberrant quiescent and senescent cell states have be...