Bioinformatics Research Center, Department of Statistics, North Carolina State University, Raleigh, NC, USA.
Center for Human Genetics Research, Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, USA.
BioData Min. 2008 Jul 17;1(1):3. doi: 10.1186/1756-0381-1-3.
During the past two decades, the field of human genetics has experienced an information explosion. The completion of the human genome project and the development of high throughput SNP technologies have created a wealth of data; however, the analysis and interpretation of these data have created a research bottleneck. While technology facilitates the measurement of hundreds or thousands of genes, statistical and computational methodologies are lacking for the analysis of these data. New statistical methods and variable selection strategies must be explored for identifying disease susceptibility genes for common, complex diseases. Neural networks (NN) are a class of pattern recognition methods that have been successfully implemented for data mining and prediction in a variety of fields. The application of NN for statistical genetics studies is an active area of research. Neural networks have been applied in both linkage and association analysis for the identification of disease susceptibility genes.In the current review, we consider how NN have been used for both linkage and association analyses in genetic epidemiology. We discuss both the successes of these initial NN applications, and the questions that arose during the previous studies. Finally, we introduce evolutionary computing strategies, Genetic Programming Neural Networks (GPNN) and Grammatical Evolution Neural Networks (GENN), for using NN in association studies of complex human diseases that address some of the caveats illuminated by previous work.
在过去的二十年中，人类遗传学领域经历了一场信息爆炸。人类基因组计划的完成和高通量 SNP 技术的发展产生了大量的数据；然而，这些数据的分析和解释却造成了研究的瓶颈。尽管技术促进了数百或数千个基因的测量，但缺乏用于分析这些数据的统计和计算方法。必须探索新的统计方法和变量选择策略，以确定常见复杂疾病的疾病易感性基因。神经网络 (NN) 是一类模式识别方法，已成功应用于各种领域的数据挖掘和预测。NN 在统计遗传学研究中的应用是一个活跃的研究领域。神经网络已应用于连锁和关联分析，以识别疾病易感性基因。在当前的综述中，我们考虑了 NN 如何用于遗传流行病学中的连锁和关联分析。我们讨论了这些初始 NN 应用的成功之处，以及之前研究中出现的问题。最后，我们介绍了进化计算策略，遗传编程神经网络 (GPNN) 和语法进化神经网络 (GENN)，用于解决之前工作中阐明的一些缺陷，在复杂人类疾病的关联研究中使用 NN。
