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RISS 인기검색어
- 검색키워드
- 저자 : Nanhui Chen (검색결과 2 건)
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Jiang Huiming,Chen Haibin,Chen Nanhui 한국통합생물학회 2020 Animal cells and systems Vol.24 No.3
Kidney renal clear cell carcinoma (KIRC) remains a significant challenge worldwide because of its poor prognosis and high mortality rate, and accurate prognostic gene signatures are urgently required for individual therapy. This study aimed to construct and validate a seven-gene signature for predicting overall survival (OS) in patients with KIRC. The mRNA expression profile and clinical data of patients with KIRC were obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC). Prognosis-associated genes were identified, and a prognostic gene signature was constructed. Then, the prognostic efficiency of the gene signature was assessed. The results obtained using data from the TCGA were validated using those from the ICGC and other online databases. Gene set enrichment analyses (GSEA) were performed to explore potential molecular mechanisms. A seven-gene signature (PODXL, SLC16A12, ZIC2, ATP2B3, KRT75, C20orf141, and CHGA) was constructed, and it was found to be effective in classifying KIRC patients into high- and low-risk groups, with significantly different survival based on the TCGA and ICGC validation data set. Cox regression analysis revealed that the seven-gene signature had an independent prognostic value. Then, we established a nomogram, including the seven-gene signature, which had a significant clinical net benefit. Interestingly, the seven-gene signature had a good performance in distinguishing KIRC from normal tissues. GSEA revealed that several oncological signatures and GO terms were enriched. This study developed a novel seven-gene signature and nomogram for predicting the OS of patients with KIRC, which may be helpful for clinicians in establishing individualized treatments.
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Implementation of the Parallel Algorithm of the Cross Power Spectral Density of Random Signals
Qi Xiong,Nanhui Chen,Meisen Pan 보안공학연구지원센터 2015 International Journal of Signal Processing, Image Vol.8 No.8
Power spectral density (PSD) is an effective way to analyze random signals. However, it is a time-consuming calculation when it deals with a rapidly growing massive data by using the serial Matlab circumstances. Though a parallel computing toolbox is involved in Matlab 2004, it is too expensive to be wildly used. According to an algorithm of PSD, a parallel algorithm of cross power spectral density (PACPSD), which was implemented using the Master-Slave parallel programming model with the support of Linux clusters and MPI,was proposed here based on the theory of Welch algorithm. The experiment results reveal that PACPSD can achieve the same accuracy as the function cpsd in Matlab do, and greatly reduce the operation cost.
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