Skin Cancer

Inferring translational efficiency from transcriptomes improves noncanonical neoantigen prioritization and cancer patient stratification.

Accurate assessment of protein translation is crucial for understanding disease variant functions, but mRNA-protein discrepancy limits transcriptomics-based clinical oncology. While ribosome profiling directly measures translation, its clinical application is constrained by cost and complexity. Deep learning models like Translatomer infer translation efficiency from RNA-seq, but whether in silico ...

Adaptive distribution-aware transformer for multi-scale visual representation learning on imbalanced and low-resolution data.

Deep learning models often struggle with class imbalance and low-resolution medical images, where critical spatial details and minority-class features are underrepresented. We introduce the Adaptive Distribution-aware Vision Transformer (AdaptiveViT), a novel hybrid CNN-Transformer architecture that unifies fine-grained local feature extraction with global contextual modelling. AdaptiveViT incorpo...

Machine learning algorithms develop a tumor-educated platelets-related gene signature to predict colorectal cancer prognosis and therapy response.

Tumor-educated platelets (TEPs) have recently emerged as an important component of liquid biopsy, yet the clinical relevance in colorectal cancer (CRC...

Single-cell transcriptomic analysis reveals the T lymphocyte microenvironment and identifies HOPX as a hub gene in membranous nephropathy.

BACKGROUND: Membranous nephropathy (MN) is an autoimmune disease characterized by immune complex deposition and progressive renal function impairment....

Single-cell multi-omics dissection of RevitalAge Markers uncovers age-dependent immunotherapy resistance and druggable targets in melanoma.

Aging-related molecular reprogramming profoundly influences melanoma progression and therapeutic sensitivity, yet underlying mechanisms remain poorly ...

Integrative spatiotemporal transcriptomics identifies a liver metastasis-related initial cell population associated with the SEMA3A-NRP1 axis in pancreatic ductal adenocarcinoma.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy in which liver metastasis represents the principal determinant of po...

Cellular architecture and neighborhood-informed virtual spatial tumor profiling from histopathology.

The tumor microenvironment (TME) critically shapes disease progression and therapeutic resistance. However, a comprehensive understanding of its spati...

Transcriptomic profiling identifies immunotherapy-responsive phenotypes in microsatellite-stable metastatic colorectal cancer.

Conventional immune checkpoint inhibitors (ICIs) remain largely ineffective in microsatellite-stable metastatic colorectal cancer (MSS mCRC), where lo...

Gold-Functionalized Multilayer Heterojunction Microarchitectures Enable High-Fidelity Serum Metabolite Profiling for Skin Cancer Subtype Classification.

Skin cancer is among the most prevalent malignancies worldwide, with non-melanoma types ranking among the top five and melanoma characterized by high ...

A novel computational framework for tumor-specific T cell antigen identification using a deep neural network.

Identifying tumor-specific T-cell antigens is essential for advancing cancer immunotherapy and enabling precision-driven, AI-assisted discovery. While...

Machine learning-based identification of basement membrane-related signature to predict recurrence and immunotherapy benefit in bladder cancer.

The basement membrane (BM) plays a critical role in regulating bladder cancer (BC) progression. However, a BM-related signature for predicting BC recu...

A radio-pathological fusion model for predicting PD-L1 expression and immunotherapy response in non-small cell lung cancer.

OBJECTIVE: This study aims to construct a multimodal fusion model (FM) based on CT and hematoxylin and eosin (H&E) stained slices to predict the PD-L1...

A myeloid immunosuppressive phenotype defines primary refractoriness to atezolizumab Plus bevacizumab in hepatocellular caarcinoma.

BACKGROUND: Despite improved outcomes with atezolizumab plus bevacizumab (A+B) in hepatocellular carcinoma (HCC), primary refractoriness (PRef), chara...

Longitudinal validation of ENLIGHT, an AI predictor of immunotherapy response and resistance, in pan-cancer cohorts.

Artificial intelligence (AI) algorithms such as ENLIGHT and DeepPT represent promising approaches to identify predictive biomarkers for immune checkpo...

Mitochondrial Metabolic Biomarkers in Periodontitis: Discovery and Clinical Validation.

INTRODUCTION AND AIMS: Mitochondrial metabolic dysregulation is associated with periodontitis (PD); however, related biomarkers remain unclear. In thi...

Toward generalizable prediction of cancer signal using a cell-free DNA language model.

Cell-free DNA can be used for early cancer detection, minimal residual disease monitoring, and post-treatment risk stratification. However, current as...

Molecular Landscape and Advanced Diagnostic Technologies for BRAF Mutations in Cancer: From Quantitative PCR and ddPCR to CRISPR-Based Platforms.

BRAF mutations are key oncogenic alterations across multiple malignancies, including melanoma, thyroid carcinoma, colorectal cancer, non-small cell lu...

New Frontiers of Skin Ultrasound in Precision Medicine.

Ultrasound has emerged as a versatile, non-invasive imaging technique in dermatology, offering real-time, high-resolution visualization of cutaneous s...

A Single-Cell Guided Machine Learning Model Predicts Response to Immune Checkpoint Inhibitors in Gastric Cancer.

Resistance to immune checkpoint inhibitors is a major clinical obstacle in the treatment of gastric cancer. Identifying drug-resistant cell population...

Deep learning applications in cancer treatment Prediction: Comprehensive research foundation for systematic review and Meta-Analysis.

Deep learning has rapidly emerged as a transformative technology in oncology, offering new capabilities in treatment response prediction and personali...