The law of inheritance is the most basic and important law in genetics, which provides an important theoretical basis for explaining biological diversity and human development. However, the traditional experiments on genetic laws are time-consuming and require a lot of humans, material, and financial resources, which seriously restricts the development of genetics research. With the in-depth development of machine learning, this paper determines the transfer matrix through the diagonal dominance matrix, combines the EM algorithm and the HMM model, creatively proposes the DIAGEMHMM algorithm, and applies it to the experimental simulation study of Mendel's law of gene segregation and the law of free combination. The algorithm has achieved very good results as shown by the results of six sets of simulation experiments of the law of gene separation and the law of free combination. Among them, the accuracy of simulation experiments in diploid reaches 100 %, and the accuracy of simulation experiments in polyploid reaches 99.8 %.

中文翻译：

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DIAGEMHMM： 基于对角线占用矩阵和 EM 算法的孟德尔遗传定律的 HMM


遗传定律是遗传学中最基本、最重要的定律，为解释生物多样性和人类发展提供了重要的理论基础。然而，传统的遗传规律实验耗时长，需要大量的人力、物力和财力，严重制约了遗传学研究的发展。随着机器学习的深入发展，本文通过对角优势矩阵确定转移矩阵，结合EM算法和HMM模型，创造性地提出了DIAGEMHMM算法，并将其应用于基因分离孟德尔定律和自由组合定律的实验仿真研究。该算法取得了非常好的结果，从基因分离定律和自由结合定律的六组模拟实验结果可以看出。其中，二倍体模拟实验准确率达到 100 %，多倍体模拟实验准确率达到 99.8 %。