AIMC Topic: Nomograms

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m6A-related genes and their role in Parkinson's disease: Insights from machine learning and consensus clustering.

Medicine
Parkinson disease (PD) is a chronic neurological disorder primarily characterized by a deficiency of dopamine in the brain. In recent years, numerous studies have highlighted the substantial influence of RNA N6-methyladenosine (m6A) regulators on var...

An Integrated Nomogram Combining Deep Learning and Radiomics for Predicting Malignancy of Pulmonary Nodules Using CT-Derived Nodules and Adipose Tissue: A Multicenter Study.

Cancer medicine
BACKGROUND: Correctly distinguishing between benign and malignant pulmonary nodules can avoid unnecessary invasive procedures. This study aimed to construct a deep learning radiomics clinical nomogram (DLRCN) for predicting malignancy of pulmonary no...

Exploring prognostic biomarkers in pathological images of colorectal cancer patients via deep learning.

The journal of pathology. Clinical research
Hematoxylin and eosin (H&E) whole slide images provide valuable information for predicting prognostic outcomes in colorectal cancer (CRC) patients. However, extracting prognostic indicators from pathological images is challenging due to the subtle co...

Leveraging SEER data through machine learning to predict distant lymph node metastasis and prognosticate outcomes in hepatocellular carcinoma patients.

The journal of gene medicine
OBJECTIVES: This study aims to develop and validate machine learning-based diagnostic and prognostic models to predict the risk of distant lymph node metastases (DLNM) in patients with hepatocellular carcinoma (HCC) and to evaluate the prognosis for ...

Machine learning and experimental validation of novel biomarkers for hypertrophic cardiomyopathy and cancers.

Journal of cellular and molecular medicine
Hypertrophic cardiomyopathy (HCM) is a hereditary cardiac disorder marked by anomalous thickening of the myocardium, representing a significant contributor to mortality. While the involvement of immune inflammation in the development of cardiac ailme...

Integrating single-cell transcriptomics and machine learning to predict breast cancer prognosis: A study based on natural killer cell-related genes.

Journal of cellular and molecular medicine
Breast cancer (BC) is the most commonly diagnosed cancer in women globally. Natural killer (NK) cells play a vital role in tumour immunosurveillance. This study aimed to establish a prognostic model using NK cell-related genes (NKRGs) by integrating ...

Identification and validation of potential genes for the diagnosis of sepsis by bioinformatics and 2-sample Mendelian randomization study.

Medicine
This integrated study combines bioinformatics, machine learning, and Mendelian randomization (MR) to discover and validate molecular biomarkers for sepsis diagnosis. Methods include differential expression analysis, weighted gene co-expression networ...

Establishing and clinically validating a machine learning model for predicting unplanned reoperation risk in colorectal cancer.

World journal of gastroenterology
BACKGROUND: Colorectal cancer significantly impacts global health, with unplanned reoperations post-surgery being key determinants of patient outcomes. Existing predictive models for these reoperations lack precision in integrating complex clinical d...