Mathematically Rigorous Results In Sequence Matching.

马里兰大学的Maricel Kann博士和她的团队有一份疾病突变的清单，但没有统计检验来确定这些突变是无害的还是致病的。与马里兰大学统计系Dohwan博士和Junyong Park博士的联合工作通过广泛的模拟证实了拟议的测试的有效性。测试使用了我们开发的渐近回归来估计BLAST统计参数，仿真表明该方法比使用Efron的局部错误发现率的方法更有效。Kann博士发表了将统计检验应用于突变数据的工作。 我们还开发了一个基准结构域数据库，MultiDomainBenchmark，用来评估搜索特定结构域的程序的结果，这些结构与具有特定功能的蛋白质相对应。

Dr Maricel Kann and her group at the University of Maryland had a list of mutations in disease, but no statistical test to decide whether the mutations were harmless or disease-causing. Joint work with the Drs DoHwan and Junyong Park in the Statistics Department at the University of Maryland established the validity of a proposed test with extensive simulations. The test used the asymptotic regression we developed for the estimation of BLAST statistical parameters, and the simulation showed that the method was more powerful than methods using Efron's local false-discovery rate. Dr Kann published work applying the statistical test to the mutational data. We also developed a benchmarking domain database, MultiDomainBenchmark, to evaluate the results of programs that search for specific domain architectures, corresponding to proteins with specific functions.