基于神经网络预测的SNP信息的剪接点识别算法研究

A splice site prediction algorithm based on SNP and neural network

随着基因组计划的完成,人们需要尽快从这些海量数据中了解基因组的结构,揭示生命的奥秘,剪接位点识别是其中的一个重要环节,然而到目前为止该问题仍未能得到很好的解决。在分析此问题时引入了第三代遗传标记单核苷酸多态性(SNP),以期探索变异对剪接机制的影响;其次,对DNA序列的数字化进行了探讨。通过实验表明,单核苷酸多态性的引入对于剪接位点识别算法的性能有着一定的影响,此外文中提出的编码方法对预测精度的提升亦有正面作用,整体效果比目前常用方法有了大幅提升。

Along with the progress of Human Genome Plan, people need to find out the genome structure from gained information. Recognition of splice sites is an essential part of this task. However, it is also a complicated problem, and satisfied results still cannot be reached. We design a splice site pre- diction algorithm based on BP network and SNP data to discover the influence of SNP on splice. In addi- tion, a new encoding method is used to convert the DNA character sequences into decimal strings. Ex- perimental results show the effectiveness of the SNP data and the new encoding method.