AIMC Topic: Biomarkers, Tumor

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Global research trends on biomarkers for cancer immunotherapy: Visualization and bibliometric analysis.

Human vaccines & immunotherapeutics
The global burden of cancer continues to grow, posing a significant public health challenge. Although cancer immunotherapy has shown significant efficacy, the response rate is not high. Therefore, the objective of our research was to identify the lat...

Raman Spectroscopy and Exosome-Based Machine Learning Predicts the Efficacy of Neoadjuvant Therapy for HER2-Positive Breast Cancer.

Analytical chemistry
Early prediction of the neoadjuvant therapy efficacy for HER2-positive breast cancer is crucial for personalizing treatment and enhancing patient outcomes. Exosomes, which play a role in tumor development and treatment response, are emerging as poten...

Utilizing Feature Selection Techniques for AI-Driven Tumor Subtype Classification: Enhancing Precision in Cancer Diagnostics.

Biomolecules
Cancer's heterogeneity presents significant challenges in accurate diagnosis and effective treatment, including the complexity of identifying tumor subtypes and their diverse biological behaviors. This review examines how feature selection techniques...

PRAF2 as a novel biomarker for breast cancer with machine learning and experimentation validation.

BMC cancer
BACKGROUND: Breast cancer (BC) is the most prevalent malignancy in women. Potential therapeutic targets for BC are of great significance. In our previous study, we found that prenylated rab acceptor 1 domain family member 2 (PRAF2) is an oncogene in ...

MiRS-HF: A Novel Deep Learning Predictor for Cancer Classification and miRNA Expression Patterns.

IEEE journal of biomedical and health informatics
Cancer classification and biomarker identification are crucial for guiding personalized treatment. To make effective use of miRNA associations and expression data, we have developed a deep learning model for cancer classification and biomarker identi...

Partial-Label Contrastive Representation Learning for Fine-Grained Biomarkers Prediction From Histopathology Whole Slide Images.

IEEE journal of biomedical and health informatics
In the domain of histopathology analysis, existing representation learning methods for biomarkers prediction from whole slide images (WSIs) face challenges due to the complexity of tissue subtypes and label noise problems. This paper proposed a novel...

Impact of glioma metabolism-related gene ALPK1 on tumor immune heterogeneity and the regulation of the TGF-β pathway.

Frontiers in immunology
BACKGROUND: Recent years have seen persistently poor prognoses for glioma patients. Therefore, exploring the molecular subtyping of gliomas, identifying novel prognostic biomarkers, and understanding the characteristics of their immune microenvironme...

Integrating single cell analysis and machine learning methods reveals stem cell-related gene S100A10 as an important target for prediction of liver cancer diagnosis and immunotherapy.

Frontiers in immunology
BACKGROUND: Hepatocellular carcinoma (LIHC) poses a significant health challenge worldwide, primarily due to late-stage diagnosis and the limited effectiveness of current therapies. Cancer stem cells are known to play a role in tumor development, met...

Insights into AI advances in immunohistochemistry for effective breast cancer treatment: a literature review of ER, PR, and HER2 scoring.

Current medical research and opinion
Breast cancer is a significant health challenge, with accurate and timely diagnosis being critical to effective treatment. Immunohistochemistry (IHC) staining is a widely used technique for the evaluation of breast cancer markers, but manual scoring ...