Proteomics
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Identification of genes and pathways involved in diseases and physiological conditions is a major task in systems biology. In this study, we developed a novel non-parameter Ising model to integrate protein-protein interaction network and microarray data for identifying differentially expressed (DE) genes. ⋯ The results showed that more cancer-related DE sub-networks and genes were identified by the Ising model than those by the Markov random field model. Furthermore, cross-validation experiments showed that DE genes identified by Ising model can improve classification performance compared with DE genes identified by Markov random field model.
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Human embryonic stem cells (hESCs) are of immense interest for regenerative medicine as a source of tissue replacement. Expansion of hESCs as a pluripotent population requires a balance between survival, proliferation and self-renewal signals. One of the key growth factors that maintains this balance is fibroblast growth factor-2 (FGF-2). ⋯ Among those are several members of the canonical pathways involved in pluripotency and self-renewal (e.g. Wnt and PI3K/AKT), hESC-associated proteins such as SOX2, RIF1, SALL4, DPPA4, DNMT3B and 53 proteins that are target genes of the pluripotency transcription factors SOX2, OCT4 and NANOG. These findings complement existing pluripotency analyses and provide new insights into how FGF-2 assists in maintaining the undifferentiated state of hESCs.