AIMC Topic: Biomarkers, Tumor

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3Mont: A multi-omics integrative tool for breast cancer subtype stratification.

PloS one
Breast Cancer (BRCA) is a heterogeneous disease, and it is one of the most prevalent cancer types among women. Developing effective treatment strategies that address diverse types of BRCA is crucial. Notably, among different BRCA molecular sub-types,...

Transcriptomic analysis reveals novel targets in benign schwannoma using machine learning.

Neuroscience
BACKGROUND & OBJECTIVE: This study aimed to identify key immune-related biomarkers of benign schwannoma through machine learning-assisted transcriptomic and single-cell analyses, and to construct a predictive model for disease evaluation.

Integrative multi-omics and machine-learning approaches uncover a novel metabolic-related signature associated with cancer-associated fibroblasts in gastric cancer development.

Computers in biology and medicine
Gastric cancer (GC) ranks as the fifth most commonly diagnosed malignancy and the fourth leading cause of cancer-related mortality worldwide. The integration of machine learning in the analysis of GC metabolomics data for biomarker identification rem...

Innovative technologies and their clinical prospects for early lung cancer screening.

Clinical and experimental medicine
BACKGROUND: Lung cancer remains the leading cause of cancer-related mortality worldwide, due to lacking effective early-stage screening approaches. Imaging, such as low-dose CT, poses radiation risk, and biopsies can induce some complications. Additi...

A multi-gene predictive model for the radiation sensitivity of nasopharyngeal carcinoma based on machine learning.

eLife
Radiotherapy resistance in nasopharyngeal carcinoma (NPC) is a major cause of recurrence and metastasis. Identifying radiotherapy-related biomarkers is crucial for improving patient survival outcomes. This study developed the nasopharyngeal carcinoma...

Predicting response to patients with gastric cancer via a dynamic-aware model with longitudinal liquid biopsy data.

Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association
BACKGROUND: Gastric cancer (GC) presents challenges in predicting treatment responses due to its patient-specific heterogeneity. Recently, liquid biopsies have emerged as a valuable data modality, offering essential cellular and molecular insights wh...

Development and interpretation of machine learning-based prognostic models for predicting high-risk prognostic pathological components in pulmonary nodules: integrating clinical features, serum tumor marker and imaging features.

Journal of cancer research and clinical oncology
BACKGROUND: With the improvement of imaging, the screening rate of Pulmonary nodules (PNs) has further increased, but their identification of High-Risk Prognostic Pathological Components (HRPPC) is still a major challenge. In this study, we aimed to ...

Classification of glioma grade and Ki-67 level prediction in MRI data: A SHAP-driven interpretation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
This study focuses on artificial intelligence-driven classification of glioma and Ki-67 leveling using T2w-FLAIR MRI, exploring the association of Ki-67 biomarkers with deep learning (DL) features through explainable artificial intelligence (XAI) and...

Artificial Intelligence in cancer epigenomics: a review on advances in pan-cancer detection and precision medicine.

Epigenetics & chromatin
DNA methylation is a fundamental epigenetic modification that regulates gene expression and maintains genomic stability. Consequently, DNA methylation remains a key biomarker in cancer research, playing a vital role in diagnosis, prognosis, and tailo...

Comprehensive statistical and machine learning framework for identification of metabolomic biomarkers in breast cancer.

Metabolomics : Official journal of the Metabolomic Society
INTRODUCTION: Breast cancer is the most common cancer among women, with its burden increasing over the past decades. Early diagnosis significantly improves survival rates and reduces lethality. Innovative technologies are being developed for early de...