AIRCC's International Journal of Computer Science and Information Technology

Open Access Open Access  Restricted Access Subscription Access

Gene-Gene Interaction Analysis in Alzheimer


Affiliations
1 Computer Science & Engineering, IIT BHU (Varanasi), India
 

Genome-wide transcription profiling is a powerful technique in studying disease susceptible footprints. Moreover, when applied to disease tissue it may reveal quantitative and qualitative alterations in gene expression that give information on the context or underlying basis for the disease and may provide a new diagnostic approach. However, the data obtained from high-density microarrays is highly complex and poses considerable challenges in data mining. Past researches prove that neuro diseases damage the brain network interaction, protein- protein interaction and gene-gene interaction. A number of neurological research paper also analyze the relationship among damaged part. Analysis of gene-gene interaction network drawn by using state-of-the-art gene database of Alzheimer’s patient can conclude a lot of information. In this paper we used gene dataset affected with Alzheimer’s disease and normal patient’s dataset from NCBI databank. After proper processing the .CEL affymetrix data using RMA, we use the processed data to find gene interaction outputs. Then we filter the output files using probe set filtering attributes p-value and fold count and draw a gene-gene interaction network. Then we analyze the interaction network using GeneMania software.

Keywords

Gene-Gene Interaction, Dementia, Alzheimer’s Disease, RMA, Genemania.
User
Notifications
Font Size

  • Koo, Ching Lee, Mei Jing Liew, Mohd Saberi Mohamad, Mohamed Salleh, and Abdul Hakim. "A review for detecting gene-gene interactions using machine learning methods in genetic epidemiology." BioMed research international 2013 (2013).

  • Sırava, M., T. Schäfer, Markus Eiglsperger, Michael Kaufmann, Oliver Kohlbacher, Erich BornbergBauer, and Hans-Peter Lenhof. "BioMiner—modeling, analyzing, and visualizing biochemical pathways and networks." Bioinformatics 18, no. suppl_2 (2002): S219-S230.

  • Meng, Ying, Susan Groth, Jill R. Quinn, John Bisognano, and Tong Tong Wu. "An Exploration of Gene-Gene Interactions and Their Effects on Hypertension." International journal of genomics 2017 (2017).

  • Musameh, Muntaser D., William YS Wang, Christopher P. Nelson, Carla Lluís-Ganella, Radoslaw Debiec, Isaac Subirana, Roberto Elosua et al. "Analysis of gene-gene interactions among common variants in candidate cardiovascular genes in coronary artery disease." PloS one10, no. 2 (2015): e0117684.

  • Gilbert‐Diamond, Diane, and Jason H. Moore. "Analysis of gene‐gene interactions." Current protocols in human genetics(2011): 1-14.

  • Cordell, Heather J. "Detecting gene–gene interactions that underlie human diseases." Nature Reviews Genetics 10, no. 6 (2009): 392.

  • Biju, K. S., S. S. Alfa, Kavya Lal, Alvia Antony, and M. Kurup Akhil. "Alzheimer’s Detection Based on Segmentation of MRI Image." Procedia Computer Science 115 (2017): 474-481.

  • Sarraf, Saman, and Ghassem Tofighi. "Classification of alzheimer's disease using fmri data and deep learning convolutional neural networks." arXiv preprint arXiv:1603.08631 (2016).

  • Caspi, Avshalom, Ahmad R. Hariri, Andrew Holmes, Rudolf Uher, and Terrie E. Moffitt. "Genetic sensitivity to the environment: the case of the serotonin transporter gene and its implications for studying complex diseases and traits." Focus 8, no. 3 (2010): 398-416.

  • Wellcome Trust Case Control Consortium. "Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls." Nature 447, no. 7145 (2007): 661.

  • Easton, Douglas F., Karen A. Pooley, Alison M. Dunning, Paul DP Pharoah, Deborah Thompson, Dennis G. Ballinger, Jeffery P. Struewing et al. "Genome-wide association study identifies novel breast cancer susceptibility loci." Nature 447, no. 7148 (2007): 1087.

  • Koo, Ching Lee, Mei Jing Liew, Mohd Saberi Mohamad, Abdul Hakim Mohamed Salleh, Safaai Deris, Zuwairie Ibrahim, Bambang Susilo, Yusuf Hendrawan, and Agustin Krisna Wardani. "Software for detecting gene-gene interactions in genome wide association studies." Biotechnology and bioprocess engineering 20, no. 4 (2015): 662-676.

  • R Hasan, Ahmed, John E Pattison, and Alex Hariz. "Pre-processing of affymetrix gene chip microarray data." Current Bioinformatics 5, no. 4 (2010): 270-279.

  • Vaes, Evelien, Mona Khan, and Peter Mombaerts. "Statistical analysis of differential gene expression relative to a fold change threshold on NanoString data of mouse odorant receptor genes." BMC bioinformatics 15, no. 1 (2014): 39.

  • Dalman, Mark R., Anthony Deeter, Gayathri Nimishakavi, and Zhong-Hui Duan. "Fold change and pvalue cutoffs significantly alter microarray interpretations." In BMC bioinformatics, vol. 13, no. 2, p. S11. BioMed Central, 2012.


Abstract Views: 347

PDF Views: 149




  • Gene-Gene Interaction Analysis in Alzheimer

Abstract Views: 347  |  PDF Views: 149

Authors

Rishi Yadav
Computer Science & Engineering, IIT BHU (Varanasi), India
Ravi Bhushan Mishra
Computer Science & Engineering, IIT BHU (Varanasi), India

Abstract


Genome-wide transcription profiling is a powerful technique in studying disease susceptible footprints. Moreover, when applied to disease tissue it may reveal quantitative and qualitative alterations in gene expression that give information on the context or underlying basis for the disease and may provide a new diagnostic approach. However, the data obtained from high-density microarrays is highly complex and poses considerable challenges in data mining. Past researches prove that neuro diseases damage the brain network interaction, protein- protein interaction and gene-gene interaction. A number of neurological research paper also analyze the relationship among damaged part. Analysis of gene-gene interaction network drawn by using state-of-the-art gene database of Alzheimer’s patient can conclude a lot of information. In this paper we used gene dataset affected with Alzheimer’s disease and normal patient’s dataset from NCBI databank. After proper processing the .CEL affymetrix data using RMA, we use the processed data to find gene interaction outputs. Then we filter the output files using probe set filtering attributes p-value and fold count and draw a gene-gene interaction network. Then we analyze the interaction network using GeneMania software.

Keywords


Gene-Gene Interaction, Dementia, Alzheimer’s Disease, RMA, Genemania.

References