Prediction of Gene Phenotypes Based on GO and KEGG Pathway Enrichment Scores.

Observing what phenotype the overexpression or knockdown of gene can cause is the basic method of investigating gene functions. Many advanced biotechnologies, such as RNAi, were developed to study the gene phenotype. But there are still many limitations. Besides the time and cost, the knockdown of s...

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Publicado en:BioMed Research International Vol. 2013; pp. 870795 - 870796
Autores principales: Zhang, Tao, Jiang, Min, Chen, Lei, Niu, Bing, Cai, Yudong
Formato: Journal Article
Publicado: Wiley-Blackwell 2013
Acceso en línea:Ver este registro en EBSCOhost
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      dt: 2013
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      pub: Wiley-Blackwell
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        atl: Prediction of Gene Phenotypes Based on GO and KEGG Pathway Enrichment Scores.
      aug:
        au:
          Zhang, Tao
          Jiang, Min
          Chen, Lei
          Niu, Bing
          Cai, Yudong
        affil: Institute of Systems Biology, Shanghai University, 99 ShangDa Road, Shanghai 200444, China.
      sug:
        subj:
          Genetic Techniques Statistics and Numerical Data
          Genetic Research Statistics and Numerical Data
          Phenotype
          Algorithms
          Resource Databases
          Models, Biological
          Genes
          Yeasts
          Yeasts Physiology
          Proteins
          Proteins Physiology
      ab: Observing what phenotype the overexpression or knockdown of gene can cause is the basic method of investigating gene functions. Many advanced biotechnologies, such as RNAi, were developed to study the gene phenotype. But there are still many limitations. Besides the time and cost, the knockdown of some gene may be lethal which makes the observation of other phenotypes impossible. Due to ethical and technological reasons, the knockdown of genes in complex species, such as mammal, is extremely difficult. Thus, we proposed a new sequence-based computational method called NNA-based method for gene phenotypes prediction. Different to the traditional sequence-based computational method, our method regards the multiphenotype as a whole network which can rank the possible phenotypes associated with the query protein and shows a more comprehensive view of the protein's biological effects. According to the prediction result of yeast, we also find some more related features, including GO and KEGG information, which are making more contributions in identifying protein phenotypes. This method can be applied in gene phenotype prediction in other species.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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