Computational analysis methods including machine learning have a significant impact in the
fields of genomics and medicine. High-throughput gene expression analysis methods such …

Pediatric cancers rarely exhibit recurrent mutational events when compared to most adult
cancers. This poses a challenge in understanding how cancers initiate, progress, and …

Background Clustering is crucial for gene expression data analysis. As an unsupervised
exploratory procedure its results can help researchers to gain insights and formulate new …

Copy number variants (CNVs) are a major component of genotypic and phenotypic variation
in genomes. To date, our knowledge of genotypic variation and evolution has largely been …

The assessment of global food security is imperative for sustainable development
worldwide. However, limited data availability has impeded a comprehensive evaluation on a …

Background Clustering of gene expression data is widely used to identify novel subtypes of
cancer. Plenty of clustering approaches have been proposed, but there is a lack of …

Cluster analysis is usually the first step adopted to unveil information from gene expression
microarray data. Besides selecting a clustering algorithm, choosing an appropriate proximity …

Alzheimer's disease (AD) is the most common form of dementia. There is no treatment and
AD models have focused on a small subset of genes identified in familial AD. Microarray …

Cancer subtype identification is important to facilitate cancer diagnosis and select effective
treatments. Clustering of cancer patients based on high-dimensional RNA-sequencing data …

This paper reviews challenges and opportunities in multiscale data integration for
biomedical informatics. Biomedical data can come from different biological origins, data …