Artificial neural networks (ANN) are computing architectures with many interconnections of simple neural-inspired computing elements, and have been applied to biomedical fields such as imaging analysis and diagnosis. We have developed a new ANN frame...
BACKGROUND: The effect of gene expression data on diagnosis remains limited. Here, we show how diagnosis and classification of growth hormone deficiency (GHD) can be achieved from a single blood sample using a combination of transcriptomics and rando...
BACKGROUND: Gene Ontology (GO) is one of the most popular bioinformatics resources. In the past decade, Gene Ontology-based gene semantic similarity has been effectively used to model gene-to-gene interactions in multiple research areas. However, mos...
Ageing has a huge impact on human health and economy, but its molecular basis - regulation and mechanism - is still poorly understood. By today, more than three hundred genes (almost all of them function as protein-coding genes) have been related to ...
BACKGROUND: Since the establishment of the first biomedical ontology Gene Ontology (GO), the number of biomedical ontology has increased dramatically. Nowadays over 300 ontologies have been built including extensively used Disease Ontology (DO) and H...
Risk stratification model for lung cancer with gene expression profile is of great interest. Instead of previous models based on individual prognostic genes, we aimed to develop a novel system-level risk stratification model for lung adenocarcinoma b...
BACKGROUND: The Gene Ontology (GO) is a community-based bioinformatics resource that employs ontologies to represent biological knowledge and describes information about gene and gene product function. GO includes three independent categories: molecu...
BACKGROUND: Stratification of patient subpopulations that respond favorably to treatment or experience and adverse reaction is an essential step toward development of new personalized therapies and diagnostics. It is currently feasible to generate om...
The advances in biological technologies make it possible to generate data for multiple conditions simultaneously. Discovering the condition-specific modules in multiple networks has great merit in understanding the underlying molecular mechanisms of ...
The application of complex network modeling to analyze large co-expression data sets has gained traction during the last decade. In particular, the use of the weighted gene co-expression network analysis framework has allowed an unbiased and systems-...
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