Display options
Cite
Malhotra A, Younesi E, Bagewadi S, et al. Linking hypothetical knowledge patterns to disease molecular signatures for biomarker discovery in Alzheimer's disease. Genome Med. 2014;6(11):97doi: 10.1186/s13073-014-0097-z.
Malhotra, A., Younesi, E., Bagewadi, S., & Hofmann-Apitius, M. (2014). Linking hypothetical knowledge patterns to disease molecular signatures for biomarker discovery in Alzheimer's disease. Genome medicine, 6(11), 97. https://doi.org/10.1186/s13073-014-0097-z
Malhotra, Ashutosh, et al. "Linking hypothetical knowledge patterns to disease molecular signatures for biomarker discovery in Alzheimer's disease." Genome medicine vol. 6,11 (2014): 97. doi: https://doi.org/10.1186/s13073-014-0097-z
Malhotra A, Younesi E, Bagewadi S, Hofmann-Apitius M. Linking hypothetical knowledge patterns to disease molecular signatures for biomarker discovery in Alzheimer's disease. Genome Med. 2014 Dec 03;6(11):97. doi: 10.1186/s13073-014-0097-z. eCollection 2014. PMID: 25484918; PMCID: PMC4256903.
Copy Download .nbib Format: NLM
Share it on

Genome Med. 2014 Dec 03;6(11):97. doi: 10.1186/s13073-014-0097-z. eCollection 2014.

Linking hypothetical knowledge patterns to disease molecular signatures for biomarker discovery in Alzheimer's disease.

Genome medicine

Ashutosh Malhotra, Erfan Younesi, Shweta Bagewadi, Martin Hofmann-Apitius

Affiliations

  1. Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53754 Sankt Augustin, Germany ; Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for Information Technology, 53113 Bonn, Germany.
  2. Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53754 Sankt Augustin, Germany.

PMID: 25484918 PMCID: PMC4256903 DOI: 10.1186/s13073-014-0097-z

Abstract

BACKGROUND: A number of compelling candidate Alzheimer's biomarkers remain buried within the literature. Indeed, there should be a systematic effort towards gathering this information through approaches that mine publicly available data and substantiate supporting evidence through disease modeling methods. In the presented work, we demonstrate that an integrative gray zone mining approach can be used as a way to tackle this challenge successfully.

METHODS: The methodology presented in this work combines semantic information retrieval and experimental data through context-specific modeling of molecular interactions underlying stages in Alzheimer's disease (AD). Information about putative, highly speculative AD biomarkers was harvested from the literature using a semantic framework and was put into a functional context through disease- and stage-specific models. Staging models of AD were further validated for their functional relevance and novel biomarker candidates were predicted at the mechanistic level.

RESULTS: Three interaction models were built representing three stages of AD, namely mild, moderate, and severe stages. Integrated analysis of these models using various arrays of evidence gathered from experimental data and published knowledge resources led to identification of four candidate biomarkers in the mild stage. Mode of action of these candidates was further reasoned in the mechanistic context of models by chains of arguments. Accordingly, we propose that some of these 'emerging' potential biomarker candidates have a reasonable mechanistic explanation and deserve to be investigated in more detail.

CONCLUSIONS: Systematic exploration of derived hypothetical knowledge leads to generation of a coherent overview on emerging knowledge niches. Integrative analysis of this knowledge in the context of disease mechanism is a promising approach towards identification of candidate biomarkers taking into consideration the complex etiology of disease. The added value of this strategy becomes apparent particularly in the area of biomarker discovery for neurodegenerative diseases where predictive biomarkers are desperately needed.

References

  1. PLoS One. 2010 Jan 01;5(1):e8556 - PubMed
  2. Biochim Biophys Acta. 2012 Jan;1824(1):89-104 - PubMed
  3. Proc Natl Acad Sci U S A. 2009 Sep 15;106(37):15938-43 - PubMed
  4. BMC Med Inform Decis Mak. 2012 Dec 18;12:148 - PubMed
  5. Lancet Neurol. 2012 Dec;11(12 ):1057-65 - PubMed
  6. Genome Res. 2003 Nov;13(11):2498-504 - PubMed
  7. Philos Trans A Math Phys Eng Sci. 2008 Sep 13;366(1878):3091-110 - PubMed
  8. Alzheimers Dement. 2013 Jul;9(4):452-458.e1 - PubMed
  9. Nat Rev Drug Discov. 2010 Jul;9(7):560-74 - PubMed
  10. Nat Rev Neurol. 2010 Mar;6(3):131-44 - PubMed
  11. J Transl Med. 2012 Oct 31;10:217 - PubMed
  12. Proc Natl Acad Sci U S A. 2012 Sep 18;109(38):15502-7 - PubMed
  13. Anat Cell Biol. 2012 Mar;45(1):26-37 - PubMed
  14. Neurotox Res. 2005;7(1-2):125-41 - PubMed
  15. Rev Neurol (Paris). 2013 Oct;169(10):719-23 - PubMed
  16. Stat Appl Genet Mol Biol. 2004;3:Article3 - PubMed
  17. J Alzheimers Dis. 2009;18(4):829-41 - PubMed
  18. J Biol Chem. 2012 Apr 20;287(17):13959-71 - PubMed
  19. EPMA J. 2013 Nov 06;4(1):23 - PubMed
  20. Alzheimers Dement. 2014 Mar;10(2):238-46 - PubMed
  21. J Pharmacol Exp Ther. 2009 Mar;328(3):813-21 - PubMed
  22. J Alzheimers Dis. 2010;22(4):1223-30 - PubMed
  23. PLoS Comput Biol. 2013;9(7):e1003117 - PubMed
  24. Arch Neurol. 2012 Feb;69(2):246-50 - PubMed
  25. Prog Neurobiol. 2011 Dec;95(4):718-28 - PubMed
  26. J Alzheimers Dis. 2011;24(1):25-34 - PubMed
  27. Int J Alzheimers Dis. 2011 Mar 31;2011:490140 - PubMed
  28. Biostatistics. 2003 Apr;4(2):249-64 - PubMed

Publication Types