Department of Biochemistry and Microbiology, School of Environmental and Biological Sciences, Rutgers University, New Brunswick, New Jersey, USA.
PLoS Comput Biol. 2013 Apr;9(4):e1002902. doi: 10.1371/journal.pcbi.1002902. Epub 2013 Apr 25.
Disease-causing aberrations in the normal function of a gene define that gene as a disease gene. Proving a causal link between a gene and a disease experimentally is expensive and time-consuming. Comprehensive prioritization of candidate genes prior to experimental testing drastically reduces the associated costs. Computational gene prioritization is based on various pieces of correlative evidence that associate each gene with the given disease and suggest possible causal links. A fair amount of this evidence comes from high-throughput experimentation. Thus, well-developed methods are necessary to reliably deal with the quantity of information at hand. Existing gene prioritization techniques already significantly improve the outcomes of targeted experimental studies. Faster and more reliable techniques that account for novel data types are necessary for the development of new diagnostics, treatments, and cure for many diseases.
导致正常基因功能异常的疾病突变将该基因定义为疾病基因。通过实验证明基因与疾病之间存在因果关系既昂贵又耗时。在进行实验测试之前,对候选基因进行全面优先级排序可以大大降低相关成本。计算基因优先级排序基于将每个基因与给定疾病相关联并暗示可能的因果关系的各种相关证据。大量证据来自于高通量实验。因此,有必要开发完善的方法来可靠地处理手头的大量信息。现有的基因优先级排序技术已经显著改善了靶向实验研究的结果。需要更快、更可靠的技术来处理新型数据类型,以便为许多疾病开发新的诊断、治疗和治愈方法。