Centre for High-Throughput Biology, Department of Computer Science, University of British Columbia, Vancouver V6T 1Z4, Canada.
Nucleic Acids Res. 2009 Nov;37(21):e139. doi: 10.1093/nar/gkp662.
Hidden Markov models (HMMs) and their variants are widely used in Bioinformatics applications that analyze and compare biological sequences. Designing a novel application requires the insight of a human expert to define the model's architecture. The implementation of prediction algorithms and algorithms to train the model's parameters, however, can be a time-consuming and error-prone task. We here present HMMConverter, a software package for setting up probabilistic HMMs, pair-HMMs as well as generalized HMMs and pair-HMMs. The user defines the model itself and the algorithms to be used via an XML file which is then directly translated into efficient C++ code. The software package provides linear-memory prediction algorithms, such as the Hirschberg algorithm, banding and the integration of prior probabilities and is the first to present computationally efficient linear-memory algorithms for automatic parameter training. Users of HMMConverter can thus set up complex applications with a minimum of effort and also perform parameter training and data analyses for large data sets.
隐马尔可夫模型（HMMs）及其变体广泛应用于生物信息学应用中，用于分析和比较生物序列。设计一个新的应用程序需要人类专家的洞察力来定义模型的架构。然而，预测算法和训练模型参数的算法的实现可能是一项耗时且容易出错的任务。我们在这里介绍 HMMConverter，这是一个用于设置概率 HMM、对 HMM 以及广义 HMM 和对 HMM 的软件包。用户通过 XML 文件定义模型本身和要使用的算法，然后该文件直接转换为高效的 C++代码。该软件包提供线性内存预测算法，如 Hirschberg 算法、带算法和先验概率的集成，并且是第一个提供用于自动参数训练的计算高效线性内存算法。因此，HMMConverter 的用户可以用最小的努力来设置复杂的应用程序，并且还可以为大型数据集执行参数训练和数据分析。
