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Int. J. Mol. Sci. 2015, 16(12), 29179-29206; doi:10.3390/ijms161226148

Bioinformatics Mining and Modeling Methods for the Identification of Disease Mechanisms in Neurodegenerative Disorders

1
Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Institutszentrum Birlinghoven, Sankt Augustin D-53754, Germany
2
Rheinische Friedrich-Wilhelms-Universitaet Bonn, University of Bonn, Bonn 53113, Germany
3
Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, and Unit of Clinical Epidemiology, Karolinska University Hospital, Stockholm SE-171 77, Sweden
4
Science for Life Laboratories, Karolinska Institutet, Stockholm SE-171 77, Sweden
5
Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 7, avenue des Hauts-Fourneaux, Esch-sur-Alzette L-4362, Luxembourg
6
Institute of Health Informatics, University College London, London NW1 2DA, UK
7
Auckland Bioengineering Institute, University of Auckland, Symmonds Street, Auckland 1142, New Zealand
8
Translational Bioinformatics, UCB Pharma, 216 Bath Rd, Slough SL1 3WE, UK
9
Translational Science Unit, SANOFI Recherche & Développement, 1 Avenue Pierre Brossolette, Chilly-Mazarin Cedex 91385, France
*
Author to whom correspondence should be addressed.
Academic Editor: Kurt A. Jellinger
Received: 16 October 2015 / Revised: 10 November 2015 / Accepted: 12 November 2015 / Published: 7 December 2015
(This article belongs to the Special Issue Mechanisms of Neurodegeneration)
View Full-Text   |   Download PDF [5138 KB, uploaded 7 December 2015]   |  

Abstract

Since the decoding of the Human Genome, techniques from bioinformatics, statistics, and machine learning have been instrumental in uncovering patterns in increasing amounts and types of different data produced by technical profiling technologies applied to clinical samples, animal models, and cellular systems. Yet, progress on unravelling biological mechanisms, causally driving diseases, has been limited, in part due to the inherent complexity of biological systems. Whereas we have witnessed progress in the areas of cancer, cardiovascular and metabolic diseases, the area of neurodegenerative diseases has proved to be very challenging. This is in part because the aetiology of neurodegenerative diseases such as Alzheimer´s disease or Parkinson´s disease is unknown, rendering it very difficult to discern early causal events. Here we describe a panel of bioinformatics and modeling approaches that have recently been developed to identify candidate mechanisms of neurodegenerative diseases based on publicly available data and knowledge. We identify two complementary strategies—data mining techniques using genetic data as a starting point to be further enriched using other data-types, or alternatively to encode prior knowledge about disease mechanisms in a model based framework supporting reasoning and enrichment analysis. Our review illustrates the challenges entailed in integrating heterogeneous, multiscale and multimodal information in the area of neurology in general and neurodegeneration in particular. We conclude, that progress would be accelerated by increasing efforts on performing systematic collection of multiple data-types over time from each individual suffering from neurodegenerative disease. The work presented here has been driven by project AETIONOMY; a project funded in the course of the Innovative Medicines Initiative (IMI); which is a public-private partnership of the European Federation of Pharmaceutical Industry Associations (EFPIA) and the European Commission (EC). View Full-Text
Keywords: mechanism-identification; bioinformatics; genetics; graphical models; knowledge-based modeling; multiscale; neurodegeneration; data integration; disease models mechanism-identification; bioinformatics; genetics; graphical models; knowledge-based modeling; multiscale; neurodegeneration; data integration; disease models
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Hofmann-Apitius, M.; Ball, G.; Gebel, S.; Bagewadi, S.; de Bono, B.; Schneider, R.; Page, M.; Kodamullil, A.T.; Younesi, E.; Ebeling, C.; Tegnér, J.; Canard, L. Bioinformatics Mining and Modeling Methods for the Identification of Disease Mechanisms in Neurodegenerative Disorders. Int. J. Mol. Sci. 2015, 16, 29179-29206.

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