Bioinformatics Mining and Modeling Methods for the Identification of Disease Mechanisms in Neurodegenerative Disorders.
Bioinformatics Mining and Modeling Methods for the Identification of Disease Mechanisms in Neurodegenerative Disorders.
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DOI：
10.3390/ijms161226148
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发表时间：
2015-12-07
影响因子：
5.6
通讯作者：
Canard L
中科院分区：
文献类型：
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作者：
Hofmann-Apitius M;Ball G;Gebel S;Bagewadi S;de Bono B;Schneider R;Page M;Kodamullil AT;Younesi E;Ebeling C;Tegnér J;Canard L
自人类基因组解码以来，生物信息学，统计学和机器学习技术一直有助于揭示应用于临床样本，动物模型和细胞系统的技术分析技术产生的越来越多数量和类型的不同数据的模式。然而，在揭示疾病的生物机制方面的进展有限，部分原因是生物系统固有的复杂性。虽然我们在癌症、心血管和代谢疾病领域取得了进展，但神经退行性疾病领域已被证明是非常具有挑战性的。这在一定程度上是因为阿尔茨海默病或帕金森病等神经退行性疾病的病因尚不清楚，因此很难辨别早期的因果事件。在这里，我们描述了一个小组的生物信息学和建模方法，最近开发的候选机制的神经退行性疾病的基础上公开可用的数据和知识。我们确定了两个互补的策略，数据挖掘技术，使用遗传数据作为起点，进一步丰富使用其他数据类型，或者编码先验知识的疾病机制在一个基于模型的框架，支持推理和富集分析。我们的综述说明了在神经病学领域，特别是神经退行性疾病领域，整合异构，多尺度和多模式信息所面临的挑战。我们的结论是，随着时间的推移，从每个患有神经退行性疾病的个体中系统收集多种数据类型的努力将加快进展。本文介绍的工作是由AETIONOMY项目推动的;该项目是创新药物倡议（IMI）资助的项目;该项目是欧洲制药工业协会联合会（EFPIA）和欧盟委员会（EC）的公私合作伙伴关系。
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).