Skin Cancer

Unveiling Varied Cell Death Patterns in Lung Adenocarcinoma Prognosis and Immunotherapy Based on Single-Cell Analysis and Machine Learning.

Programmed cell death (PCD) pathways hold significant influence in the etiology and progression of a...

Integrated machine learning developed a prognosis-related gene signature to predict prognosis in oesophageal squamous cell carcinoma.

The mortality rate of oesophageal squamous cell carcinoma (ESCC) remains high, and conventional TNM ...

NeoaPred: a deep-learning framework for predicting immunogenic neoantigen based on surface and structural features of peptide-human leukocyte antigen complexes.

MOTIVATION: Neoantigens, derived from somatic mutations in cancer cells, can elicit anti-tumor immun...

Melanoma imaging and diagnosis: What does the future hold?

BACKGROUND: In Australia, artificial intelligence (AI) is increasingly being used in the field of me...

Patient-led skin cancer teledermatology without dermoscopy during the COVID-19 pandemic: important lessons for the development of future patient-facing teledermatology and artificial intelligence-assisted -self-diagnosis.

MySkinSelfie is a mobile phone application for skin self-monitoring, enabling secure sharing of pati...

TransCell: In Silico Characterization of Genomic Landscape and Cellular Responses by Deep Transfer Learning.

Gene expression profiling of new or modified cell lines becomes routine today; however, obtaining co...

Unravelling tumour cell diversity and prognostic signatures in cutaneous melanoma through machine learning analysis.

Melanoma, a highly malignant tumour, presents significant challenges due to its cellular heterogenei...

Deciphering lung adenocarcinoma prognosis and immunotherapy response through an AI-driven stemness-related gene signature.

Lung adenocarcinoma (LUAD) is a leading cause of cancer-related deaths, and improving prognostic acc...

Integrating machine learning and single-cell analysis to uncover lung adenocarcinoma progression and prognostic biomarkers.

The progression of lung adenocarcinoma (LUAD) from atypical adenomatous hyperplasia (AAH) to invasiv...

Advancing lung adenocarcinoma prognosis and immunotherapy prediction with a multi-omics consensus machine learning approach.

Lung adenocarcinoma (LUAD) is a tumour characterized by high tumour heterogeneity. Although there ar...

Using artificial intelligence-based technologies to support the diagnosis and early detection of melanoma in primary care.

Evaluation of an artificial intelligence-based decision support for the detection of cutaneous melanoma in primary care: a prospective real-life clinical trial.

BACKGROUND: Use of artificial intelligence (AI), or machine learning, to assess dermoscopic images o...

The Immunopeptidomics Ontology (ImPO).

The adaptive immune response plays a vital role in eliminating infected and aberrant cells from the ...

Improving Prediction of Survival and Progression in Metastatic Non-Small Cell Lung Cancer After Immunotherapy Through Machine Learning of Circulating Tumor DNA.

PURPOSE: To use modern machine learning approaches to enhance and automate the feature extraction fr...

Characterization of unique pattern of immune cell profile in patients with nasopharyngeal carcinoma through flow cytometry and machine learning.

In patients with nasopharyngeal carcinoma (NPC), the alteration of immune responses in peripheral bl...

Unravelling the metabolic landscape of cutaneous melanoma: Insights from single-cell sequencing analysis and machine learning for prognostic assessment of lactate metabolism.

This manuscript presents a comprehensive investigation into the role of lactate metabolism-related g...

A novel deep learning framework for accurate melanoma diagnosis integrating imaging and genomic data for improved patient outcomes.

BACKGROUND: Melanoma is one of the most malignant forms of skin cancer, with a high mortality rate i...

Comprehensive single-cell RNA-seq analysis using deep interpretable generative modeling guided by biological hierarchy knowledge.

Recent advances in microfluidics and sequencing technologies allow researchers to explore cellular h...

HLAIImaster: a deep learning method with adaptive domain knowledge predicts HLA II neoepitope immunogenic responses.

While significant strides have been made in predicting neoepitopes that trigger autologous CD4+ T ce...

Machine Learning Links T-cell Function and Spatial Localization to Neoadjuvant Immunotherapy and Clinical Outcome in Pancreatic Cancer.

Tumor molecular data sets are becoming increasingly complex, making it nearly impossible for humans ...

Comprehensive analysis of clinical images contributions for melanoma classification using convolutional neural networks.

BACKGROUND: Timely diagnosis plays a critical role in determining melanoma prognosis, prompting the ...