Biodata Mining

BioAug-Net: a bioimage sensor-driven attention-augmented segmentation framework with physiological coupling for early prostate cancer detection in T2-weighted MRI.

MultiChem: predicting chemical properties using multi-view graph attention network.

Exploring glioma heterogeneity through omics networks: from gene network discovery to causal insights and patient stratification.

Machine learning approaches to identify systemic lupus erythematosus in anti-nuclear antibody-positive patients using genomic data and electronic health records

使用基因组数据和电子健康记录的机器学习方法识别抗核抗体阳性患者的系统性红斑狼疮

Unsupervised encoding selection through ensemble pruning for biomedical classification.

DeepAutoGlioma: a deep learning autoencoder-based multi-omics data integration and classification tools for glioma subtyping.

Machine learning based study for the classification of Type 2 diabetes mellitus subtypes.

基于机器学习的2型糖尿病亚型分类研究

Machine learning approaches for the genomic prediction of rheumatoid arthritis and systemic lupus erythematosus

用于类风湿性关节炎和系统性红斑狼疮基因组预测的机器学习方法

Exploration of a diversity of computational and statistical measures of association for genome-wide genetic studies.

Grasping frequent subgraph mining for bioinformatics applications.