Ovarian Cancer

Deep learning based on ultrasound images to predict platinum resistance in patients with epithelial ovarian cancer.

BACKGROUND: The study aimed at developing and validating a deep learning (DL) model based on the ultrasound imaging for predicting the platinum resistance of patients with epithelial ovarian cancer (EOC).

OmicsCL: Unsupervised Contrastive Learning for Cancer Subtype Discovery and Survival Stratification

Unsupervised learning of disease subtypes from multi-omics data presents a significant opportunity for advancing personalized medicine. We introduce OmicsCL, a modular contrastive learning framework that jointly embeds heterogeneous omics modalities-such as gene expression, DNA methylation, and miRNA expression-into a unified latent space. Our method incorporates a survival-aware contrastive los...

Multimodal Deep Learning for Subtype Classification in Breast Cancer Using Histopathological Images and Gene Expression Data

Molecular subtyping of breast cancer is crucial for personalized treatment and prognosis. Traditional classification approaches rely on either histo...

Nonlinear Sparse Generalized Canonical Correlation Analysis for Multi-view High-dimensional Data

Motivation: Biomedical studies increasingly produce multi-view high-dimensional datasets (e.g., multi-omics) that demand integrative analysis. Exist...

Multivariate Feature Selection and Autoencoder Embeddings of Ovarian Cancer Clinical and Genetic Data

This study explores a data-driven approach to discovering novel clinical and genetic markers in ovarian cancer (OC). Two main analyses were performe...

ICFNet: Integrated Cross-modal Fusion Network for Survival Prediction

Survival prediction is a crucial task in the medical field and is essential for optimizing treatment options and resource allocation. However, curre...

TCUP – An Open Access Tool to Predict Tissue of Origin and Cancer of Unknown Primary (CUP)

Cancer of unknown primary (CUP) remains a major diagnostic hurdle, compromising therapies that depend on accurately identifying tissue of origin. We p...

Characterization of metabolic phenotypes in breast cancer through the integration of genome-scale metabolic models and machine learning

The metabolic heterogeneity of breast cancer represents a significant challenge for the identification of biomarkers and therapeutic targets. To addre...

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...

SpliceSelectNet: A Hierarchical Transformer-Based Deep Learning Model for Splice Site Prediction

Accurate RNA splicing is essential for gene expression and protein function, yet the mechanisms governing splice site recognition remain incompletely ...

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...

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 ...

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...

A quantitative comparison between human experts and AI at estimating tumor-stroma ratio

The tumor–stroma ratio (TSR) is an established prognostic biomarker across several cancer types, yet its manual assessment remains labour-intensive an...

IHGAMP: Pan-cancer HRD prediction from routine H&E whole-slide images using foundation models

Homologous recombination deficiency (HRD) confers sensitivity to poly (ADP-ribose) polymerase (PARP) inhibitors and platinum-based chemotherapy, repre...

GAIN-BRCA: a graph-based AI-net framework for breast cancer subtype classification using multiomics data.

MOTIVATION: Contextual integration of multiomic datasets from the same patient could improve the accuracy of subtype prediction algorithms to help wit...

A Novel Effective Models for Identifying BRCA Patients and Optimizing Clinical Treatments.

OBJECTIVE: This study aimed to develop an effective model that identifies high-risk breast cancer (BRCA) patients and optimizes clinical treatments.

TPD52 as a Therapeutic Target Identified by Machine Learning Shapes the Immune Microenvironment in Breast Cancer.

Breast cancer (BRCA) is one of the most common malignancies and a leading cause of cancer-related mortality among women globally. Despite advances in ...

Patherea: Cell Detection and Classification for the 2020s

This paper presents a Patherea, a framework for point-based cell detection and classification that provides a complete solution for developing and e...

PathoGen-X: A Cross-Modal Genomic Feature Trans-Align Network for Enhanced Survival Prediction from Histopathology Images

Accurate survival prediction is essential for personalized cancer treatment. However, genomic data - often a more powerful predictor than pathology ...