Woodhams M D, Hendy M D
Allan Wilson Centre for Molecular Ecology and Evolution, Massey University, Palmerston North, New Zealand.
Bioinformatics. 2004 Aug 4;20 Suppl 1:i348-54. doi: 10.1093/bioinformatics/bth926.
Maximum likelihood (ML) for phylogenetic inference from sequence data remains a method of choice, but has computational limitations. In particular, it cannot be applied for a global search through all potential trees when the number of taxa is large, and hence a heuristic restriction in the search space is required. In this paper, we derive a quadratic approximation, QAML, to the likelihood function whose maximum is easily determined for a given tree. The derivation depends on Hadamard conjugation, and hence is limited to the simple symmetric models of Kimura and of Jukes and Cantor. Preliminary testing has demonstrated the accuracy of QAML is close to that of ML.
从序列数据进行系统发育推断时,最大似然法(ML)仍然是一种首选方法,但存在计算限制。特别是,当分类单元数量很大时,它无法应用于对所有潜在树进行全局搜索,因此需要在搜索空间中进行启发式限制。在本文中,我们推导了似然函数的二次近似QAML,其最大值对于给定的树很容易确定。该推导依赖于哈达玛共轭,因此仅限于木村模型以及朱克斯和坎托的简单对称模型。初步测试表明,QAML的准确性与ML相近。