Bioinformatics Mining and Modeling Methods for the Identification of Disease Mechanisms in Neurodegenerative Disorders
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 cellul...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2015-12-01
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| Series: | International Journal of Molecular Sciences |
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| Online Access: | http://www.mdpi.com/1422-0067/16/12/26148 |
| _version_ | 1811204806068404224 |
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| author | Martin Hofmann-Apitius Gordon Ball Stephan Gebel Shweta Bagewadi Bernard de Bono Reinhard Schneider Matt Page Alpha Tom Kodamullil Erfan Younesi Christian Ebeling Jesper Tegnér Luc Canard |
| author_facet | Martin Hofmann-Apitius Gordon Ball Stephan Gebel Shweta Bagewadi Bernard de Bono Reinhard Schneider Matt Page Alpha Tom Kodamullil Erfan Younesi Christian Ebeling Jesper Tegnér Luc Canard |
| author_sort | Martin Hofmann-Apitius |
| collection | DOAJ |
| description | 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). |
| first_indexed | 2024-04-12T03:19:49Z |
| format | Article |
| id | doaj.art-c9828c177f1e45cab0bda7a82287c50c |
| institution | Directory Open Access Journal |
| issn | 1422-0067 |
| language | English |
| last_indexed | 2024-04-12T03:19:49Z |
| publishDate | 2015-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | International Journal of Molecular Sciences |
| spelling | doaj.art-c9828c177f1e45cab0bda7a82287c50c2022-12-22T03:49:57ZengMDPI AGInternational Journal of Molecular Sciences1422-00672015-12-011612291792920610.3390/ijms161226148ijms161226148Bioinformatics Mining and Modeling Methods for the Identification of Disease Mechanisms in Neurodegenerative DisordersMartin Hofmann-Apitius0Gordon Ball1Stephan Gebel2Shweta Bagewadi3Bernard de Bono4Reinhard Schneider5Matt Page6Alpha Tom Kodamullil7Erfan Younesi8Christian Ebeling9Jesper Tegnér10Luc Canard11Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Institutszentrum Birlinghoven, Sankt Augustin D-53754, GermanyUnit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, and Unit of Clinical Epidemiology, Karolinska University Hospital, Stockholm SE-171 77, SwedenLuxembourg Centre for Systems Biomedicine, University of Luxembourg, 7, avenue des Hauts-Fourneaux, Esch-sur-Alzette L-4362, LuxembourgDepartment of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Institutszentrum Birlinghoven, Sankt Augustin D-53754, GermanyInstitute of Health Informatics, University College London, London NW1 2DA, UKLuxembourg Centre for Systems Biomedicine, University of Luxembourg, 7, avenue des Hauts-Fourneaux, Esch-sur-Alzette L-4362, LuxembourgTranslational Bioinformatics, UCB Pharma, 216 Bath Rd, Slough SL1 3WE, UKRheinische Friedrich-Wilhelms-Universitaet Bonn, University of Bonn, Bonn 53113, GermanyDepartment of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Institutszentrum Birlinghoven, Sankt Augustin D-53754, GermanyDepartment of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Institutszentrum Birlinghoven, Sankt Augustin D-53754, GermanyUnit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, and Unit of Clinical Epidemiology, Karolinska University Hospital, Stockholm SE-171 77, SwedenTranslational Science Unit, SANOFI Recherche & Développement, 1 Avenue Pierre Brossolette, Chilly-Mazarin Cedex 91385, FranceSince 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).http://www.mdpi.com/1422-0067/16/12/26148mechanism-identificationbioinformaticsgeneticsgraphical modelsknowledge-based modelingmultiscaleneurodegenerationdata integrationdisease models |
| spellingShingle | Martin Hofmann-Apitius Gordon Ball Stephan Gebel Shweta Bagewadi Bernard de Bono Reinhard Schneider Matt Page Alpha Tom Kodamullil Erfan Younesi Christian Ebeling Jesper Tegnér Luc Canard Bioinformatics Mining and Modeling Methods for the Identification of Disease Mechanisms in Neurodegenerative Disorders International Journal of Molecular Sciences mechanism-identification bioinformatics genetics graphical models knowledge-based modeling multiscale neurodegeneration data integration disease models |
| title | Bioinformatics Mining and Modeling Methods for the Identification of Disease Mechanisms in Neurodegenerative Disorders |
| title_full | Bioinformatics Mining and Modeling Methods for the Identification of Disease Mechanisms in Neurodegenerative Disorders |
| title_fullStr | Bioinformatics Mining and Modeling Methods for the Identification of Disease Mechanisms in Neurodegenerative Disorders |
| title_full_unstemmed | Bioinformatics Mining and Modeling Methods for the Identification of Disease Mechanisms in Neurodegenerative Disorders |
| title_short | Bioinformatics Mining and Modeling Methods for the Identification of Disease Mechanisms in Neurodegenerative Disorders |
| title_sort | bioinformatics mining and modeling methods for the identification of disease mechanisms in neurodegenerative disorders |
| topic | mechanism-identification bioinformatics genetics graphical models knowledge-based modeling multiscale neurodegeneration data integration disease models |
| url | http://www.mdpi.com/1422-0067/16/12/26148 |
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