Skin Cancer

Feature selection enhances peptide binding predictions for TCR-specific interactions.

INTRODUCTION: T-cell receptors (TCRs) play a critical role in the immune response by recognizing specific ligand peptides presented by major histocompatibility complex (MHC) molecules. Accurate prediction of peptide binding to TCRs is essential for advancing immunotherapy, vaccine design, and understanding mechanisms of autoimmune disorders.

Colorimetric aptasensor coupled with a deep-learning-powered smartphone app for programmed death ligand-1 expressing extracellular vesicles.

Lung cancer is a devastating public health threat and a leading cause of cancer-related deaths. Therefore, it is imperative to develop sophisticated techniques for the non-invasive detection of lung cancer. Extracellular vesicles expressing programmed death ligand-1 (PD-L1) markers (PD-L1@EVs) in the blood are reported to be indicative of lung cancer and response to immunotherapy. Our approach is ...

Embracing the Future of Clinical Trials in Radiation Therapy: An NRG Oncology CIRO Technology Retreat Whitepaper on Pioneering Technologies and AI-Driven Solutions.

This white paper examines the potential of pioneering technologies and artificial intelligence-driven solutions in advancing clinical trials involving...

Minimal sourced and lightweight federated transfer learning models for skin cancer detection.

One of the most fatal diseases that affect people is skin cancer. Because nevus and melanoma lesions are so similar and there is a high likelihood of ...

Comparative analysis of deep learning and radiomic signatures for overall survival prediction in recurrent high-grade glioma treated with immunotherapy.

BACKGROUND: Radiomic analysis of quantitative features extracted from segmented medical images can be used for predictive modeling of prognosis in bra...

Advanced Deep Learning Models for Melanoma Diagnosis in Computer-Aided Skin Cancer Detection.

The most deadly type of skin cancer is melanoma. A visual examination does not provide an accurate diagnosis of melanoma during its early to middle st...

Computational biology and artificial intelligence in mRNA vaccine design for cancer immunotherapy.

Messenger RNA (mRNA) vaccines offer an adaptable and scalable platform for cancer immunotherapy, requiring optimal design to elicit a robust and targe...

Exploring the anticancer activities of Sulfur and magnesium oxide through integration of deep learning and fuzzy rough set analyses based on the features of Vidarabine alkaloid.

Drug discovery and development is a challenging and time-consuming process. Laboratory experiments conducted on Vidarabine showed IC 6.97 µg∕mL, 25.78...

Cellular Senescence in Hepatocellular Carcinoma: Immune Microenvironment Insights via Machine Learning and In Vitro Experiments.

Hepatocellular carcinoma (HCC), a leading liver tumor globally, is influenced by diverse risk factors. Cellular senescence, marked by permanent cell c...

Leveraging Deep Learning for Immune Cell Quantification and Prognostic Evaluation in Radiotherapy-Treated Oropharyngeal Squamous Cell Carcinomas.

The tumor microenvironment plays a critical role in cancer progression and therapeutic responsiveness, with the tumor immune microenvironment (TIME) b...

Automatic machine learning accurately predicts the efficacy of immunotherapy for patients with inoperable advanced non-small cell lung cancer using a computed tomography-based radiomics model.

PURPOSE: Patients with advanced non-small cell lung cancer (NSCLC) have varying responses to immunotherapy, but there are no reliable, accepted biomar...

Diagnosis and prognosis of melanoma from dermoscopy images using machine learning and deep learning: a systematic literature review.

BACKGROUND: Melanoma is a highly aggressive skin cancer, where early and accurate diagnosis is crucial to improve patient outcomes. Dermoscopy, a non-...

Construction of an anaplastic thyroid cancer stratification signature to guide immune therapy selection and validation of the pivotal gene HLF through experiments.

INTRODUCTION: While most thyroid cancer patients have a favorable prognosis, anaplastic thyroid carcinoma (ATC) remains a particularly aggressive form...

The role of mitophagy-related genes in prognosis and immunotherapy of cutaneous melanoma: a comprehensive analysis based on single-cell RNA sequencing and machine learning.

Mitophagy, the selective degradation of mitochondria by autophagy, plays a crucial role in cancer progression and therapy response. This study aims to...

The KMeansGraphMIL Model: A Weakly Supervised Multiple Instance Learning Model for Predicting Colorectal Cancer Tumor Mutational Burden.

Colorectal cancer (CRC) is one of the top three most lethal malignancies worldwide, posing a significant threat to human health. Recently proposed imm...

A novel machine learning-based immune prognostic signature for improving clinical outcomes and guiding therapy in colorectal cancer: an integrated bioinformatics and experimental study.

Immune cells are pivotal components in the tumor microenvironment (TME), which can interact with tumor cells and significantly influence cancer progre...

CA19-9-related macrophage polarization drives poor prognosis in HCC after immune checkpoint inhibitor treatment.

BACKGROUND: Elevated levels of carbohydrate antigen 19-9 (CA19-9) levels are known to worsen outcomes in various tumors by influencing immune response...

A Symbolic AI Approach to Medical Training.

In traditional medical education, learners are mostly trained to diagnose and treat patients through supervised practice. Artificial Intelligence and ...

Advancing precision cancer immunotherapy drug development, administration, and response prediction with AI-enabled Raman spectroscopy.

Molecular characterization of tumors is essential to identify predictive biomarkers that inform treatment decisions and improve precision immunotherap...

A vision-language foundation model for precision oncology.

Clinical decision-making is driven by multimodal data, including clinical notes and pathological characteristics. Artificial intelligence approaches t...