在硅基因发现。

Bing Yu
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引用次数: 3

摘要

复杂疾病可能涉及多种基因和环境因素的相互作用。发现这些基因是困难的,基于计算机的策略可以显著提高它们的检测。数据挖掘和自动跟踪新知识有助于轨迹映射。在基因搜索阶段,候选基因的计算机优先排序在连锁或关联位点的处理中起着不可或缺的作用。计算机分析也可以区分编码DNA变异的微妙后果,并且仍然是预测非编码DNA变异功能的主要方法,特别是在启动子区域的那些。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
In silico gene discovery.

Complex diseases can involve the interaction of multiple genes and environmental factors. Discovering these genes is difficult, and in silico based strategies can significantly improve their detection. Data mining and automated tracking of new knowledge facilitate locus mapping. At the gene search stage, in silico prioritization of candidate genes plays an indispensable role in dealing with linked or associated loci. In silico analysis can also differentiate subtle consequences of coding DNA variants and remains the major method to predict functionality for non-coding DNA variants, particularly those in promoter regions.

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