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Disease Informatics

  • Sayak Ganguli
  • Abhijit Datta
Chapter

Abstract

The field of disease informatics around the world has focused on the application of information technology to understand and prevent disease outbreaks. The recent Ebola outbreak has again pointed out our deficiencies in the proper management and vaccination practices around the world in situations of a pandemic disease. However analyses at the molecular level targeting the proteins and other immune system components provide us with the opportunity to identify effective targets for small molecule-based targeting as well as to understand the biology of the disease. Several therapeutic approaches around the world are being explored. Starting from traditional chemical medicine to ethnomedicinal practices, small molecule compound libraries are being screened using virtual screening procedures for the quest to identify and predict lead molecules of the future having limited side effects but increased efficacy. Apart from small molecule-based therapeutic strategies, oligonucleotide- and aptamer-based strategies are also being explored which enables us to directly interfere with the genome function of a particular pathogen. Combinatorial libraries and high-throughput practices such as next-generation sequencing have also accelerated the discovery of information and genetic medicine or personalized medicine which looked like a distant dream a few years back but is gradually transforming into reality. In this era of information generation, at the big data level, it is imperative that informatics-based strategies be explored and utilized fully so as to manage and analyze information in real time. Bioinformatics and clinical informatics approaches are continuously being utilized for providing patient health-care support, and scientists around the globe are working round the clock to tackle diseases from the epidemiological, molecular, and post-medical phases. Basic science research investigating the biology of the diseases is also being funded since it forms the stepping stones on which the entire discipline of disease informatics stands firm. This chapter deals with three different diseases (Parkinson’s disease, influenza, and AIDS (caused by HIV 1)) and how various bioinformatics approaches help us to understand the biology of the disease and its effects. Each case study provides a putative translational output of the disease management which can be utilized by researchers in the clinical trial phase.

Keywords

Diseases microRNAs Ebola Parkinson’s Disease Natural Products 

References

  1. Bakry R et al (2007) Medicinal applications of fullerenes. Int J Neuromed 2(4):639–649Google Scholar
  2. Banik R, Ganguli S, Datta A (2013) HIV-1 genome analyses reveals conserved Musashi binding elements (MBE) – possible roles in glioblastoma multiforme. Int J Comput Bioinforma In Silico Model 2(6):293–296Google Scholar
  3. Buehler J et al (2004) Framework for evaluating public health surveillance systems for early detection of outbreaks: recommendations from the CDC Working Group. Morb Mortal Wkly Rep 53(RR-5):1–13Google Scholar
  4. Chen X, Reynolds CH (2002) Performance of similarity measures in 2D fragment-based similarity searching: comparison of structural descriptors and similarity coefficients. J Chem Inf Comput Sci 42:1407–1414CrossRefPubMedGoogle Scholar
  5. Cordato DJ, Chan DK (2004) Genetics and Parkinson’s disease. J Clin Neuro Sci 2:119–123CrossRefGoogle Scholar
  6. Cui SX et al (2006) Curcumin inhibits telomerase activity in human cancer cell lines. Int J Mol Med 18:227–231PubMedGoogle Scholar
  7. Damianos L et al (2002) MiTAP for bio-security: a case study. AI Mag 23(4):13–29Google Scholar
  8. Dev KK, Hofele K, Barbieri S, Buchman VL, van der Putten H (2003) Part II: alpha-synuclein and its molecular pathophysiological role in neurodegenerative disease. Neuropharmacology 45:14–44CrossRefPubMedGoogle Scholar
  9. Diamandis EP, Yousef GM, Luo LY, Magklara A, Obiezu CV (2000) The new human kallikrein gene family: implications in carcinogenesis. Trends Endocrinol Metab 11:54–60CrossRefPubMedGoogle Scholar
  10. Doss-Pepe EW, Chen L, Madura K (2005) Alpha-synuclein and parkin contribute to the assembly of ubiquitin lysine 63-linked multi ubiquitin chains. J Biol Chem 17:16619–16624CrossRefGoogle Scholar
  11. Eidson M (2001) Neon needles in a haystack: the advantages of passive surveillance for West Nile virus. In: White DJ, Morse DL (eds) West Nile virus: detection, surveillance, and control. New York Academy of Sciences, New York, pp 38–53Google Scholar
  12. Eidson M et al (2001) Dead crow densities and human cases of West Nile Virus, New York State, 2000. Emerg Infect Dis 7:662.664PubMedCentralGoogle Scholar
  13. Galperin YM et al (2015) The 2015 nucleic acids research database issue and molecular biology database collection. Nucl Acids Res 43(Database issue):D1–D5CrossRefPubMedGoogle Scholar
  14. Ganguli S, Mitra S, Datta A (2011) Antagomirbase: a putative antagomir database. Bioinformation 7(1):41–43CrossRefPubMedPubMedCentralGoogle Scholar
  15. Gao D, Sakurai K, Katoh M (1996) Inhibition of microsomal lipid peroxidation by baicalein: a possible formation of an iron-baicalein complex. Biochem Mol Biol Int 39:215PubMedGoogle Scholar
  16. Guptill SC et al (2003) Early-season avian deaths from West Nile virus as warnings of human infection. Emerg Infect Dis 9:483.484CrossRefPubMedCentralGoogle Scholar
  17. Hyman J, LaForce T (2003) Modeling the spread of influenza among cities. In: Banks H, Castillo-Chavez C (eds) Bioterrorism: Mathematical modeling applications in homeland security Chapter 10. Society for Industrial and Applied Mathematics, p 211–236CrossRefGoogle Scholar
  18. Julian KG et al (2002) Early season crow mortality as a sentinel for West Nile virus disease in humans, northeastern United States. Vector Borne Zoonotic Dis 2:145–155CrossRefPubMedGoogle Scholar
  19. Liontas A, Yeger H (2004) Curcumin and resveratrol induce apoptosis and nuclear translocation and activation of p53 in human neuroblastoma. Anticancer Res 24:987–998PubMedGoogle Scholar
  20. Lipinski CA (2000) Drug-like properties and the causes of poor solubility and poor permeability. J Pharmacol Toxicol Methods 44:235–249CrossRefPubMedGoogle Scholar
  21. Matsuzaki Y et al (1996) Cell death induced by baicalein in human hepatocellular carcinoma cell lines. Jpn J Cancer Res 87:170–177CrossRefPubMedPubMedCentralGoogle Scholar
  22. Roy P et al (2011) Structural analysis of predicted hiv-1 SECIS elements. World J AIDS 1:208–218CrossRefGoogle Scholar
  23. Thimm M, Goede A, Hougardy S, Preissner R (2004) Comparison of 2D similarity and 3D superposition. Application to searching a conformational drug database. J Chem Inf Comput Sci 44:1816–1822CrossRefPubMedGoogle Scholar
  24. Yim H et al (1999) Emodin, an anthraquinone derivative isolated from the rhizomes of Rheum palmatum, selectively inhibits the activity of casein kinase II as a competitive inhibitor. Planta Med 65:9–13CrossRefPubMedGoogle Scholar
  25. Youssef KM, El-Sherbeny MA (2005) Synthesis and antitumor activity of some curcumin analogs. Arch Pharm (Weinheim) 338:181–189CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Sayak Ganguli
    • 1
  • Abhijit Datta
    • 2
  1. 1.Theoretical and Computational Biology DivisionAmplicon Institute of Interdisciplinary Science and TechnologyPaltaIndia
  2. 2.Department of BotanyJhargram Raj CollegeMedinipurIndia

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