Modeling of Spatial Distribution of Knockout Effect for Genes Associated With Aggressiveness of Low Grade Glioma in Human Brain Tissues Using Machine Learning

Mathematical Biology & Bioinformatics. 2014;9(2):534-542.

doi: 10.17537/2014.9.534.

1. Hawrylycz MJ, Lein ES, Guillozet-Bongaarts A, Shen EH, Ng L, Miller JA, van de Lagemaat LN, Smith KA, Ebbert A, Riley ZL et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature. 2012;489(7416):391-399. doi: 10.1038/nature11405
2. Heintz N. Gene expression nervous system atlas (GENSAT). Nature Neuroscience. 2004;7(5):483-483. doi: 10.1038/nn0504-483
3. Magdaleno S, Jensen P, Brumwell CL, Seal A, Lehman K, Asbury A, Cheung T, Cornelius T, Batten DM, Eden C et al. BGEM: an in situ hybridization database of gene expression in the embryonic and adult mouse nervous system. PLoS Biology. 2006;4(4):e86. doi: 10.1371/journal.pbio.0040086
4. Edgar R, Domrachev M, Lash AE. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Research. 2002;30(1):207-210. doi: 10.1093/nar/30.1.207
5. De Jong H. Modeling and simulation of genetic regulatory systems: a literature review. Journal of Computational Biology. 2002;9(1):67-103. doi: 10.1089/10665270252833208
6. Someren EV, Wessels LFA, Backer E, Reinders MJT. Genetic network modeling. Pharmacogenomics. 2002;3(4):507-525. doi: 10.1517/14622416.3.4.507
7. Hecker M, Lambeck S, Toepfer S, van Someren E, Guthke R. Gene regulatory network inference: data integration in dynamic models - a review. Biosystems. 2009;96(1):86-103. doi: 10.1016/j.biosystems.2008.12.004
8. Husmeier D. Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks. Bioinformatics. 2003;19(17):2271-2282. doi: 10.1093/bioinformatics/btg313
9. Rogers S, Girolami MA. Bayesian regression approach to the inference of regulatory networks from gene expression data. Bioinformatics. 2005;21(14):3131-3137. doi: 10.1093/bioinformatics/bti487
10. Steggles LJ, Banks R, Shaw O, Wipat A. Qualitatively modelling and analysing genetic regulatory networks: a Petri net approach. Bioinformatics. 2007;23(3):336-343. doi: 10.1093/bioinformatics/btl596
11. Duffau H, Capelle L. Preferential brain locations of low-grade gliomas. Cancer. 2004;100(12):2622-2626. doi: 10.1002/cncr.20297
12. Pouratian N, Schiff D. Management of low-grade glioma. Current Neurology and Neuroscience Reports. 2010;10(3):224-231. doi: 10.1007/s11910-010-0105-7
13. Franceschini A, Szklarczyk D, Frankild S, Kuhn M, Simonovic M, Roth A, Lin J, Minguez P, Bork P, von Mering C, Jensen LJ. STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Research. 2013;41(D1):808-815. doi: 10.1093/nar/gks1094
14. Cui X, Li J. XjVie, a viewing program for SPM. http://people.hnl.bcm.tmc.edu/cuixu/xjView/ (accessed 11 September 2014).
15. Penny W, Friston K, Ashburner J, Kiebel S, Nichols T. Statistical Parametric Mapping: The Analysis of Functional Brain Images. 2007. 656 p.
16. Ivanisenko NV, Gubanova NV, Kolchanov NA, Ivanisenko VA. Identification of Gene Markers for Aggressive Forms of Low-Grade Glioma Using TCGA Expression Data. Matematicheskaya biologiya i bioinformatika (Mathematical Biology and Bioinformatics). 2014;9(2):527-533 (in Russ.). doi: 10.17537/2014.9.527