Skin Cancer

Artificial Intelligence-Powered Molecular Docking and Steered Molecular Dynamics for Accurate scFv Selection of Anti-CD30 Chimeric Antigen Receptors.

Chimeric antigen receptor (CAR) T cells represent a revolutionary immunotherapy that allows specific tumor recognition by a unique single-chain fragment variable (scFv) derived from monoclonal antibodies (mAbs). scFv selection is consequently a fundamental step for CAR construction, to ensure accurate and effective CAR signaling toward tumor antigen binding. However, conventional in vitro and in v...

Integrating machine learning algorithms and multiple immunohistochemistry validation to unveil novel diagnostic markers based on costimulatory molecules for predicting immune microenvironment status in triple-negative breast cancer.

INTRODUCTION: Costimulatory molecules are putative novel targets or potential additions to current available immunotherapy, but their expression patterns and clinical value in triple-negative breast cancer (TNBC) are to be clarified.

Integrated multi-omics analysis and machine learning to refine molecular subtypes, prognosis, and immunotherapy in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) has a malignant characteristic that is highly aggressive and prone to metastasis. There is still a lack of suitable biomark...

Integrated machine learning algorithms reveal a bone metastasis-related signature of circulating tumor cells in prostate cancer.

Bone metastasis is an essential factor affecting the prognosis of prostate cancer (PCa), and circulating tumor cells (CTCs) are closely related to dis...

TransfIGN: A Structure-Based Deep Learning Method for Modeling the Interaction between HLA-A*02:01 and Antigen Peptides.

The intricate interaction between major histocompatibility complexes (MHCs) and antigen peptides with diverse amino acid sequences plays a pivotal rol...

Artificial intelligence and skin melanoma.

Melanoma is the deadliest skin cancer, presenting typically with changing pigmented areas and usually treated with surgical removal. As benign cutaneo...

Identification of metastasis-related genes for predicting prostate cancer diagnosis, metastasis and immunotherapy drug candidates using machine learning approaches.

BACKGROUND: Prostate cancer (PCa) is the second leading cause of tumor-related mortality in men. Metastasis from advanced tumors is the primary cause ...

Integrated analysis of single-cell sequencing and machine learning identifies a signature based on monocyte/macrophage hub genes to analyze the intracranial aneurysm associated immune microenvironment.

Monocytes are pivotal immune cells in eliciting specific immune responses and can exert a significant impact on the progression, prognosis, and immuno...

Integrative deep learning with prior assisted feature selection.

Integrative analysis has emerged as a prominent tool in biomedical research, offering a solution to the "small and large " challenge. Leveraging the...

Multi omics analysis of mitophagy subtypes and integration of machine learning for predicting immunotherapy responses in head and neck squamous cell carcinoma.

Mitophagy serves as a critical mechanism for tumor cell death, significantly impacting the progression of tumors and their treatment approaches. There...

Bluish veil detection and lesion classification using custom deep learnable layers with explainable artificial intelligence (XAI).

Melanoma, one of the deadliest types of skin cancer, accounts for thousands of fatalities globally. The bluish, blue-whitish, or blue-white veil (BWV)...

Identifying novel circadian rhythm biomarkers for diagnosis and prognosis of melanoma by an integrated bioinformatics and machine learning approach.

Melanoma is a highly malignant skin tumor with poor prognosis. Circadian rhythm is closely related to melanoma pathogenesis. This study aimed to ident...

Ultrasensitive plasma-based monitoring of tumor burden using machine-learning-guided signal enrichment.

In solid tumor oncology, circulating tumor DNA (ctDNA) is poised to transform care through accurate assessment of minimal residual disease (MRD) and t...

A machine learning radiomics based on enhanced computed tomography to predict neoadjuvant immunotherapy for resectable esophageal squamous cell carcinoma.

BACKGROUND: Patients with resectable esophageal squamous cell carcinoma (ESCC) receiving neoadjuvant immunotherapy (NIT) display variable treatment re...

Developing a prognostic model using machine learning for disulfidptosis related lncRNA in lung adenocarcinoma.

Disulfidptosis represents a novel cell death mechanism triggered by disulfide stress, with potential implications for advancements in cancer treatment...

Optimized attention-induced multihead convolutional neural network with efficientnetv2-fostered melanoma classification using dermoscopic images.

Melanoma is an uncommon and dangerous type of skin cancer. Dermoscopic imaging aids skilled dermatologists in detection, yet the nuances between melan...

New vision of HookEfficientNet deep neural network: Intelligent histopathological recognition system of non-small cell lung cancer.

BACKGROUND: Efficient and precise diagnosis of non-small cell lung cancer (NSCLC) is quite critical for subsequent targeted therapy and immunotherapy....

Integration of deep learning and habitat radiomics for predicting the response to immunotherapy in NSCLC patients.

BACKGROUND: The non-invasive biomarkers for predicting immunotherapy response are urgently needed to prevent both premature cessation of treatment and...

TumFlow: An AI Model for Predicting New Anticancer Molecules.

Melanoma is the fifth most common cancer in the United States. Conventional drug discovery methods are inherently time-consuming and costly, which imp...

Automatized self-supervised learning for skin lesion screening.

Melanoma, the deadliest form of skin cancer, has seen a steady increase in incidence rates worldwide, posing a significant challenge to dermatologists...