CFP last date
20 January 2025
Reseach Article

Protein Network for Associating Genes with Dementia

by Brijendra Gupta, Ravi Bhushan Mishra
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 83 - Number 10
Year of Publication: 2013
Authors: Brijendra Gupta, Ravi Bhushan Mishra
10.5120/14486-2795

Brijendra Gupta, Ravi Bhushan Mishra . Protein Network for Associating Genes with Dementia. International Journal of Computer Applications. 83, 10 ( December 2013), 29-35. DOI=10.5120/14486-2795

@article{ 10.5120/14486-2795,
author = { Brijendra Gupta, Ravi Bhushan Mishra },
title = { Protein Network for Associating Genes with Dementia },
journal = { International Journal of Computer Applications },
issue_date = { December 2013 },
volume = { 83 },
number = { 10 },
month = { December },
year = { 2013 },
issn = { 0975-8887 },
pages = { 29-35 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume83/number10/14486-2795/ },
doi = { 10.5120/14486-2795 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:59:30.658846+05:30
%A Brijendra Gupta
%A Ravi Bhushan Mishra
%T Protein Network for Associating Genes with Dementia
%J International Journal of Computer Applications
%@ 0975-8887
%V 83
%N 10
%P 29-35
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Association between causal genes and their genetic diseases is an important problem concerning human health. Linkage analysis is such a method that can identify which unknown disease genes are located in chromosomal region out of hundreds of candidate genes according to their functions, interactions, and pathways which is good identification of genes associated with general/hereditary disorders. Here, we used method for prioritization of candidate genes of Dementia by the use of a global network distance measure, Random Walk Analysis, which detects neurological disorder been associated with distribution of sub-network among the genes.

References
  1. Franke,L. et al.
  2. . Reconstruction of a functional human gene network, with an application for prioritizing positional candidate genes. Am. J. Hum. Genet. , 78, 1011–1025.
  3. Gaulton,K. J. et al.
  4. Acomputational system to select candidate genes for complex human traits. Bioinformatics, 23, 1132–1140.
  5. Perez-Iratxeta,C. et al.
  6. Update of the G2D tool for prioritization of gene candidates to inherited diseases. Nucleic Acids Res. , 35, W212–W216.
  7. Aerts,S. et al.
  8. Gene prioritization through genomic data fusion. Nat. Biotechnol. , 24, 537–544.
  9. Sam,L. et al.
  10. Discovery of protein interaction networks shared by diseases. In Pacific Symposium on Biocomputing. World Scientific, Singapore, pp. 76–87.
  11. Radivojac,P. et al.
  12. An integrated approach to inferring gene-disease associations in humans. Proteins, 72, 1030–1037.
  13. Karni,S. et al.
  14. A network-based method for predicting disease-causing genes. J. Comput. Biol. , 16, 181–189.
  15. Ma,X. et al.
  16. CGI: a new approach for prioritizing genes by combining gene expression and protein-protein interaction data. Bioinformatics, 23, 215–221.
  17. George,R. A. et al.
  18. Analysis of protein sequence and interaction data for candidate disease gene prediction. Nucleic Acids Res. , 34, e130.
  19. Ozgur,A. et al.
  20. Identifying gene-disease associations using centrality on a literature mined gene-interaction network. Bioinformatics, 24, i277–i285.
  21. van Driel,M. A. et al.
  22. A text-mining analysis of the human phenome. Eur. J. Hum. Genet. , 14, 535–542.
  23. Perez-Iratxeta, C. , Bork, P. , and Andrade, M. A.
  24. . Association of genes to genetically inherited diseases using data mining. Nat. Genet. 31, 316–319.
  25. Lo´pez-Bigas, N. , and Ouzounis, C. A.
  26. . Genome-wide identification of genes likely to be involved in human genetic disease. Nucleic Acids Res. 32, 3108–3114.
  27. Oti, M. , Snel, B. , Huynen, M. , and Brunner, H. G.
  28. . Predicting disease genes using protein–protein interactions. J. Med. Genet. 43, 691–698.
  29. Huynen,M. , Snel,B. , Lathe,W. 3rd and Bork,P.
  30. Predicting protein function by genomic context: quantitative evaluation and qualitative inferences. Genome Res. , 10, 1204–1210.
  31. Eisenberg,D. , Marcotte,E. M. , Xenarios,I. and Yeates,T. O.
  32. Protein function in the post-genomic era. Nature, 405, 823–826.
  33. Lee,I. , Blom,U. M. , Wang,P. I. , Shim,J. E. and Marcotte,M.
  34. Prioritizing candidate disease genes by network-based boosting of genome-wide association data. Genome Res. , 21, 1109–1121.
  35. Moreau,Y. and Tranchevent,L. C.
  36. Computational tools for prioritizing candidate genes: boosting disease gene discovery. Nat. Rev. Genet. , 13, 523–536.
  37. Piro,R. M. and Di Cunto,F.
  38. Computational approaches to disease-gene prediction: rationale, classification and successes. FEBS J. , 279, 678–696.
  39. Lee, J. M. , and Sonnhammer, E. L. L.
  40. . Genomic gene clustering analysis of pathways in eukaryotes. Genome Res. 13, 875–882.
  41. McKusick,V.
  42. Mendelian inheritance in man and its online version, OMIM. Am. J. Hum. Genet. , 80, 588–604.
  43. Hamosh, A. , Scott, A. F. , Amberger, J. , Bocchini, C. , Valle, D. , and McKusick, V. A.
  44. . Online mendelian inheritance in man [OMIM], a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 30, 52–55.
  45. Kohler,S. , Bauer,S. , Horn,D. and Robinson,P. N.
  46. Walking the interactome for prioritization of candidate disease genes. Am. J. Hum. Genet. , 82, 949–958.
  47. Kamburov,A. ,Pentchev,K. ,Galicka,H. Wierling, C. ,Lehrach,H. andHerwig,R.
  48. ConsensusPathDB: toward a more complete picture of cell biology. Nucleic Acids Res. , 39, D712–D717.
  49. Niu,Y. , Otasek,D. and Jurisica,I.
  50. Evaluation of linguistic features useful in extraction of interactions from PubMed;application to annotating known, high-throughput and predicted interactions in I2D. Bioinformatics, 26, 111–119.
  51. Hu,Z. ,Hung,J. H. ,Wang,Y. ,Chang,Y. C. ,Huang,C. L. Huyck,M. and DeLisi,C.
  52. VisANT 3. 5: multi-scale networkvisualization, analysis and inference based on the gene ontology. Nucleic Acids Res. , 37, W115–W121.
  53. Elefsinioti,A. ,Sarac,O. S. ,Hegele,A. ,Plake,C. ,Hubner,N. C. ,Poser,I. , Sarov,M. , Hyman,A. , Mann,M. , Schroeder,M. et al.
  54. Large-scale de novo prediction of physical protein-proteinassociation. Mol. Cell. Proteomics, 10, M111 010629.
  55. Patil,A. ,Nakai,K. andNakamura,H.
  56. HitPredict: a database of quality assessed protein-protein interactions in nine species. Nucleic Acids Res. , 39, D744–D749.
  57. Balaji,S. , McClendon,C. , Chowdhary,R. , Liu,J. S. and Zhang,J.
  58. IMID: integrated molecular interaction database. Bioinformatics, 28, 747–749.
  59. Wong,A. K. , Park,C. Y. , Greene,C. S. , Bongo,L. A. , Guan,Y. and Troyanskaya,O. G.
  60. IMP: a multi-species functional genomics portal for integration, visualization and prediction of protein functions and networks. Nucleic Acids Res. , 40,W484–W490.
  61. Saric,J. , Jensen,L. J. , Ouzounova,R. , Rojas,I. and Bork,P.
  62. Extraction of regulatory gene/protein networks from Medline. Bioinformatics, 22, 645–650.
  63. Walhout,A. J. , Sordella,R. , Lu,X. , Hartley,J. L. , Temple,G. F. ,Brasch,M. A. , Thierry-Mieg,N. and Vidal,M.
  64. Protein interaction mapping in C. elegans using proteins involved in vulval development. Science, 287, 116–122.
  65. Skrabanek,L. , Saini,H. K. , Bader,G. D. and Enright,A. J.
  66. Computational prediction of protein-protein interactions. Mol. Biotechnol. , 38, 1–17.
  67. Harrington,E. D. , Jensen,L. J. and Bork,P.
  68. Predicting biological networks from genomic data. FEBS Lett. , 582, 1251–1258.
  69. Tatusov,R. L. , Natale,D. A. , Garkavtsev,I. V. , Tatusova,T. A. ,Shankavaram,U. T. ,Rao,B. S. ,Kiryutin,B. , Galperin,M. Y. ,Fedorova,N. D. and Koonin,E. V.
  70. The COG database: new developments in phylogenetic classification of proteins from complete genomes. Nucleic Acids Res. , 29, 22–28.
  71. Jensen,L. J. , Julien,P. , Kuhn,M. , von Mering,C. , Muller,J. ,Doerks,T. and Bork,P.
  72. eggNOG: automated construction and annotation of orthologous groups of genes. Nucleic Acids Res. ,36, D250–D254.
  73. Kuhn,M. , von Mering,C. , Campillos,M. , Jensen,L. J. and Bork,P.
  74. STITCH: interaction networks of chemicals and proteins. Nucleic Acids Res. , 36, D684–D688.
  75. Linding,R. ,Jensen,L. J. ,Ostheimer,G. J. ,vanVugt,M. A. ,Jorgensen,C. , Miron,I. M. , Diella,F. , Colwill,K. , Taylor,L. , Elder,K. et al.
  76. Systematic discovery of in vivo phosphorylation networks. Cell, 129, 1415–1426.
  77. Brohee,S. , Faust,K. , Lima-Mendez,G. , Sand,O. , Janky,R. ,Vanderstocken,G. , Deville,Y. and van Helden,J.
  78. NeAT: a toolbox for the analysis of biological networks, clusters, classes and pathways. Nucleic Acids Res. , 36, W444–W451.
  79. Ashburner,M. , Ball,C. A. , Blake,J. A. , Botstein,D. , Butler,H. ,Cherry,J. M. , Davis,A. P. , Dolinski,K. , Dwight,S. S. , Eppig,J. T. et al. (2000) Gene ontology: tool for the unification of biology. The GeneOntology Consortium. Nat. Genet. , 25, 25–29.
Index Terms

Computer Science
Information Sciences

Keywords

Genetic diseases Dementia Random Walk Analysis Neurological disorder .