Skin Cancer

Automated melanoma detection. An algorithm inspired from human intelligence characterizing disordered pattern of melanocytic lesions improving a convolutional neural network.

Histological interpretation of spitzoid tumours: an extensive machine learning-based concordance analysis for improving decision making.

The histopathological classification of melanocytic tumours with spitzoid features remains a challen...

Advancing predictive markers in lung adenocarcinoma: A machine learning-based immunotherapy prognostic prediction signature.

The prognosis of lung adenocarcinoma (LUAD) is generally poor. Immunotherapy has emerged as a promis...

Machine learning-based integration develops a stress response stated T cell (Tstr)-related score for predicting outcomes in clear cell renal cell carcinoma.

BACKGROUND: Establishment of a reliable prognostic model and identification of novel biomarkers are ...

Boosting Clear Cell Renal Carcinoma-Specific Drug Discovery Using a Deep Learning Algorithm and Single-Cell Analysis.

Clear cell renal carcinoma (ccRCC), the most common subtype of renal cell carcinoma, has the high he...

A muti-modal feature fusion method based on deep learning for predicting immunotherapy response.

Immune checkpoint therapy (ICT) has greatly improved the survival of cancer patients in the past few...

A probabilistic knowledge graph for target identification.

Early identification of safe and efficacious disease targets is crucial to alleviating the tremendou...

Sa-TTCA: An SVM-based approach for tumor T-cell antigen classification using features extracted from biological sequencing and natural language processing.

Accurately predicting tumor T-cell antigen (TTCA) sequences is a crucial task in the development of ...

The selective deployment of AI in healthcare: An ethical algorithm for algorithms.

Machine-learning algorithms have the potential to revolutionise diagnostic and prognostic tasks in h...

Inference of Developmental Hierarchy and Functional States of Exhausted T Cells from Epigenetic Profiles with Deep Learning.

Exhausted T cells are a key component of immune cells that play a crucial role in the immune respons...

GraphMHC: Neoantigen prediction model applying the graph neural network to molecular structure.

Neoantigens are tumor-derived peptides and are biomarkers that can predict prognosis related to immu...

Non-invasive prediction for pathologic complete response to neoadjuvant chemoimmunotherapy in lung cancer using CT-based deep learning: a multicenter study.

Neoadjuvant chemoimmunotherapy has revolutionized the therapeutic strategy for non-small cell lung c...

Advancements in technology for characterizing the tumor immune microenvironment.

Immunotherapy plays a key role in cancer treatment, however, responses are limited to a small number...

Optimizing time prediction and error classification in early melanoma detection using a hybrid RCNN-LSTM model.

Skin cancer is a terrifying disorder that affects all individuals. Due to the significant increase i...

Machine learning framework develops neutrophil extracellular traps model for clinical outcome and immunotherapy response in lung adenocarcinoma.

Neutrophil extracellular traps (NETs) are novel inflammatory cell death in neutrophils. Emerging stu...

Exploring the NRF2-TP53 Signaling Network Through Machine Learning and Pan-Cancer Analysis: Identifying Potential targets for Cancer Prognosis Related to Oxidative Stress.

Oxidative stress (OXS) is closely related to tumor prognosis and immune response, while TP53 integra...

Decoding the glycoproteome: a new frontier for biomarker discovery in cancer.

Cancer early detection and treatment response prediction continue to pose significant challenges. Ca...

RETRACTED: Refining molecular subtypes and risk stratification of ovarian cancer through multi-omics consensus portfolio and machine learning.

Ovarian cancer (OC), known for its pronounced heterogeneity, has long evaded a unified classificatio...

Enhancing skin lesion classification with advanced deep learning ensemble models: a path towards accurate medical diagnostics.

Skin cancer, including the highly lethal malignant melanoma, poses a significant global health chall...

Prediction of immunotherapy response in idiopathic membranous nephropathy using deep learning-pathological and clinical factors.

BACKGROUND: Owing to individual heterogeneity, patients with idiopathic membranous nephropathy (IMN)...

An integrative machine learning model for the identification of tumor T-cell antigens.

The escalating global incidence of cancer poses significant health challenges, underscoring the need...