DeepKEGG: a multi-omics data integration framework with biological insights for cancer recurrence prediction and biomarker discovery.

Deep learning-based multi-omics data integration methods have the capability to reveal the mechanisms of cancer development, discover cancer biomarkers and identify pathogenic targets. However, current methods ignore the potential correlations between samples in integrating multi-omics data. In addition, providing accurate biological explanations still poses significant challenges due to the complexity of deep learning models.

IBPGNET: lung adenocarcinoma recurrence prediction based on neural network interpretability.

Lung adenocarcinoma (LUAD) is the most common histologic subtype of lung cancer. Early-stage patients have a 30-50% probability of metastatic recurrence after surgical treatment. Here, we propose a new computational framework, Interpretable Biological Pathway Graph Neural Networks (IBPGNET), based on pathway hierarchy relationships to predict LUAD recurrence and explore the internal regulatory mechanisms of LUAD.

[The value of diffusion tensor imaging and fiber tractography in cervical spondylotic myelopathy].

Objective: To study the diagnostic value of diffusion tensor imaging (DTI) in cervical spondylotic myelopathy.

Methods: Twenty healthy volunteers and fifty patients with cervical spondylotic myelopathy underwent DTI in the Affiliated Hospital of Medical College of Ningbo University from January 2014 to April 2015. Healthy volunteers served as controls.

Using R2* values to evaluate brain tumours on magnetic resonance imaging: preliminary results.

Objective: To determine the usefulness of the R2* value in assessing the histopathological grade of glioma at magnetic resonance imaging and differentiating various brain tumours.

Methods: Sixty-four patients with brain tumours underwent R2* mapping and diffusion-weighted imaging examinations. ANOVA was performed to analyse R2* values among four groups of glioma and among high-grade gliomas (grades III and IV), low-grade gliomas (grades I and II), meningiomas, and brain metastasis.

A case-control study on risk factors of breast cancer in China.

Introduction: To screen the risk factors associated with breast cancer among Chinese women in order to evaluate the individual risk of developing breast cancer among women in China.

Material And Methods: A case-control study on 416 breast cancer patients and 1156 matched controls was conducted in 14 hospitals in 8 provinces of China in 2008. Controls were age- and region-matched to the cases.