Skin Cancer

MLMarker: a machine learning framework for tissue inference and biomarker discovery.

MLMarker is a machine learning tool that computes continuous tissue similarity scores for proteomics data, addressing the challenge of interpreting complex or sparse datasets. Trained on 34 healthy tissues, its Random Forest model generates probabilistic predictions with SHAP-based protein-level explanations. A penalty factor corrects for missing proteins, improving robustness for low-coverage sam...

Impact on Cost and Expert Time of Data-Efficient Deep Learning for Medical Image Segmentation.

Purpose To develop and systematically evaluate an iterative training approach, termed the expert-guided annotation loop, for efficient reference standard medical image segmentation, including assessment of two sample-selection strategies and real-world clinical implementation. Materials and Methods This retrospective study included ten datasets comprising 1948 CT or MRI examinations from autosomal...

FFixR: A Machine Learning Framework for Accurate Somatic Mutation Calling from FFPE RNA-Seq Data in Cancer.

MOTIVATION: Formalin-fixed paraffin-embedded (FFPE) tissues are widely used in clinical and research settings, yet their use for detecting somatic mut...

Molecular characteristics and modifications of glioblastoma affecting the efficacy of immunotherapy.

Glioblastoma is a highly aggressive brain tumor characterized by complex genetic, molecular, and epigenetic features that present significant challeng...

Genetically engineered outer membrane vesicles as next-generation bionanomaterials for therapeutic and diagnostic applications.

Outer membrane vesicles (OMVs), naturally released by Gram-negative bacteria, represent a unique class of bionanomaterials with inherent immunogenicit...

Machine learning-based integration develops a novel lysosome-related prognostic signature associated with prognosis and immune infiltration landscape in acute myeloid leukemia.

BACKGROUND: Lysosomes are essential for intracellular degradation and recycling, and changes in their function significantly contribute to tumor growt...

Scenario-based multi-parametric QC for quality control of complex algorithms used in clinical care.

Complex algorithms and prediction models are increasingly being developed, investigated and applied for clinical use in medical laboratories and beyon...

Expression of the High Affinity Neurotensin Receptor Defines an Immune‑cold, Poor‑prognosis Colorectal Cancer Subtype.

OBJECTIVE: We investigated neurotensin receptor 1 (NTSR1) as a potential mediator of the mechanical immune barrier that contributes to T-cell exclusio...

Rational design of active pharmaceutical ingredient-based co-assembled nanoparticles for cancer immunotherapy.

Combination strategy is crucial for enhancing cancer therapeutic efficacy, but co-delivery of multiple active pharmaceutical ingredients (APIs) remain...

Single-cell and multi-omics integration delineates the landscape of gene-wise intratumor heterogeneity and identifies prognostic biomarkers for immunotherapy in non-small cell lung cancer.

BACKGROUND: Gene-wise intratumor heterogeneity (ITH), defined as spatial variability in the expression of individual genes across tumor regions, remai...

A HRH1-YAP1 feedback loop drives pancreatic cancer progression and predicts therapeutic response.

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy with a dismal prognosis, characterized by an immunosuppressive tumor microenviro...

2-Hydroxyisobutyrylation and Phosphorylation Crosstalk Guides Metastasis Prediction and Immunotherapy in Esophageal Squamous Cell Carcinoma.

Posttranslational modification (PTM) is pivotal in cancer progression. However, the mechanisms, biological function, and clinical significance underly...

Exploring the immune landscape and immunotherapy relevance of ER stress-related lncRNAs in melanoma through multi-omics and explainable machine learning.

BACKGROUND: Melanoma is a highly aggressive malignancy with poor clinical outcomes, and endoplasmic reticulum (ER) stress plays an important role in t...

Dissecting T-cell exhaustion heterogeneity and immune ecosystem dynamics in colorectal cancer through multi-omics machine learning.

BACKGROUND: Immunotherapy has shown limited efficacy in a substantial subset of CRC patients, yet the mechanisms underlying therapeutic resistance rem...

Body composition radiomics integrated with machine learning to predict prognosis in advanced NSCLC treated with first-line immunochemotherapy and concurrent radiotherapy.

OBJECTIVE: Adipose tissue is a highly heterogeneous and plastic endocrine and immune organ whose characteristics have been linked to prognosis in NSCL...

AI-driven cellular immunotherapy: transforming CAR-T engineering and translation.

Chimeric antigen receptor T (CAR-T) cell therapy has demonstrated remarkable success in hematologic malignancies but faces persistent challenges in so...

Data-driven system identification in cancer systems biology: A multiscale modeling approach to melanoma.

BACKGROUND AND OBJECTIVE: Developing systems biology models of cancer is critical for improving understanding of complex biological interactions and a...

Computational prediction of TCR cross-reactivity: principles, challenges and translational opportunities.

T-cell receptor (TCR) cross-reactivity, whereby a single TCR recognizes multiple peptide-MHC (pMHC) ligands, is essential for immune surveillance but ...

Deep Learning of CT Imaging Predicts PD-L1 Expression and Immunotherapy Response in Metastatic NSCLC: A Multi-Center Study.

Immune checkpoint inhibitors (ICIs) benefit only a subset of patients with metastatic non-small cell lung cancer (NSCLC), but current selection relies...

Mapping the TCR landscape: computational tools empowering translational immunology and therapy design.

T cell receptors (TCRs) are central to adaptive immunity, yet their vast sequence and structural diversity present a significant challenge to fully un...