Pathway Network Analysis of Complex Diseases Based on Multiple Biological Networks.

Biological pathways play important roles in the development of complex diseases, such as cancers, which are multifactorial complex diseases that are usually caused by multiple disorders gene mutations or pathway. It has become one of the most important issues to analyze pathways combining multiple t...

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Publicado en:BioMed Research International Vol. 2018; pp. 1 - 13
Autores principales: Zheng, Fang, Wei, Le, Zhao, Liang, Ni, FuChuan
Formato: equations & formulas research tables/charts Journal Article
Publicado: Wiley-Blackwell 7/30/2018
Acceso en línea:Ver este registro en EBSCOhost
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      dt: 7/30/2018
      vid: 2018
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      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        10.1155/2018/5670210
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        atl: Pathway Network Analysis of Complex Diseases Based on Multiple Biological Networks.
      aug:
        au:
          Zheng, Fang
          Wei, Le
          Zhao, Liang
          Ni, FuChuan
        affil: College of Informatics, Huazhong Agricultural University, Wuhan 430079, China
      sug:
        subj:
          Social Networking
          Disease Diagnosis
          Human
          Biological Markers
          Mutation
          Genomics
          Proteomics
          Phenotype
          Genes
      ab: Biological pathways play important roles in the development of complex diseases, such as cancers, which are multifactorial complex diseases that are usually caused by multiple disorders gene mutations or pathway. It has become one of the most important issues to analyze pathways combining multiple types of high-throughput data, such as genomics and proteomics, to understand the mechanisms of complex diseases. In this paper, we propose a method for constructing the pathway network of gene phenotype and find out disease pathogenesis pathways through the analysis of the constructed network. The specific process of constructing the network includes, firstly, similarity calculation between genes expressing data combined with phenotypic mutual information and GO ontology information, secondly, calculating the correlation between pathways based on the similarity between differential genes and constructing the pathway network, and, finally, mining critical pathways to identify diseases. Experimental results on Breast Cancer Dataset using this method show that our method is better. In addition, testing on an alternative dataset proved that the key pathways we found were more accurate and reliable as biological markers of disease. These results show that our proposed method is effective.
      pubtype: Academic Journal
      doctype:
        equations & formulas
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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