214

Bioinformatics of the Brain

8.8

Conclusion

In this chapter, the methods for analyzing the brain transcriptome, the repos-

itories utilized to store the information obtained using these high throughput

methods, as well as the tools for data processing and visualization, have been

covered. The advantages and disadvantages of each technique have been put

forward. Furthermore, examples of recent microarray and RNA-seq studies

on the disorders discussed in this book are given. A number of recommenda-

tions have been made that could aid researchers in better understanding the

data now available on brain disease and disorders, including merging various

methodologies (imaging techniques, transcriptomics data, and artificial intel-

ligence techniques). Future viewpoints have also been presented to guide the

research in this area.

Bibliography

[1] A. Bayat, “Science, medicine, and the future: Bioinformatics,” BMJ,

vol. 324 7344, pp. 1018–22, 2002.

[2] L. Hunt, “Margaret o. dayhoff 1925-1983,” DNA, vol. 2 2, pp. 97–8,

1983.

[3] T. M. Morse, “Neuroinformatics: From bioinformatics to databasing the

brain,” Bioinformatics and Biology Insights, vol. 2, pp. 253–264, 2008.

[4] F. M. Giorgi, C. Ceraolo, and D. Mercatelli, “The r language: An engine

for bioinformatics and data science,” Life, vol. 12, 2022.

[5] M. Fourment and M. R. Gillings, “A comparison of common program-

ming languages used in bioinformatics,” BMC Bioinformatics, vol. 9,

pp. 82–82, 2008.

[6] R. G. T. Lowe, N. J. Shirley, M. R. Bleackley, et al., “Transcriptomics

technologies,” PLoS Computational Biology, vol. 13, 2017.

[7] T. Lenoir and E. Giannella, “The emergence and diffusion of dna mi-

croarray technology,” Journal of Biomedical Discovery and Collabora-

tion, vol. 1, pp. 11–11, 2006.

[8] R. Govindarajan, J. Duraiyan, K. Kaliyappan, et al., “Microarray and

its applications,” Journal of Pharmacy & Bioallied Sciences, vol. 4,

pp. S310–S312, 2012.