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Abstract
In the past few decades, embryonic stem cells (ESCs) were of great interest as a model system for studying early developmental processes and because of their potential therapeutic applications in regenerative medicine. However, the underlying mechanisms of ESC differentiation remain unclear, which limits our exploration of the therapeutic potential of stem cells. Fortunately, the increasing quantity and diversity of biological datasets can provide us with opportunities to explore the biological secrets. However, taking advantage of diverse biological information to facilitate the advancement of ESC research still remains a challenge. Here, we propose a scalable, efficient and flexible function prediction framework that integrates diverse biological information using a simple weighted strategy, for uncovering the genetic determinants of mouse ESC differentiation. The advantage of this approach is that it can make predictions based on dynamic information fusion, owing to the simple weighted strategy. With this approach, we identified 30 genes that had been reported to be associated with differentiation of stem cells, which we regard to be associated with differentiation or pluripotency in embryonic stem cells. We also predicted 70 genes as candidates for contributing to differentiation, which requires further confirmation. As a whole, our results showed that this strategy could be applied as a useful tool for ESC research.
Highlights
Embryonic stem cells (ESCs) are unspecialized cells that have the ability of self-renewal, producing daughter cells with equivalent developmental potential, or to differentiate into more specialized cells
The annotations were arranged in a hierarchical manner and compiled using up-to-date information from Gene Ontology (GO)’s three ontology divisions, including Molecular Function (MF), Biological Process (BP) and Cellular Component (CC)
We evaluated the actual false discovery rate (FDR) of a differentially expressed genes (DEGs) list detected in simulated small samples, according to the predefined DEGs
Summary
Embryonic stem cells (ESCs) are unspecialized cells that have the ability of self-renewal, producing daughter cells with equivalent developmental potential, or to differentiate into more specialized cells. Initial approaches derive predictions based on specific information such as gene expression profile [3] and protein-protein interactions [2]. Undirected graphs are constructed based on each data source respectively, with genes as vertices and functional relationships between gene pairs as edges. These undirected graphs are integrated into a weighted functional linked network. The genes are predicted to be differentiation associated genes based on their degrees in the final network, which are regarded to be associated with differentiation or pluripotency in embryonic stem cells
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