Liu Linjing, Wong Ka-Chun
Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong.
Methods Mol Biol. 2021;2212:337-345. doi: 10.1007/978-1-0716-0947-7_21.
Complex disease is different from Mendelian disorders. Its development usually involves the interaction of multiple genes or the interaction between genes and the environment (i.e. epistasis). Although the high-throughput sequencing technologies for complex diseases have produced a large amount of data, it is extremely difficult to analyze the data due to the high feature dimension and the combination in the epistasis analysis. In this work, we introduce machine learning methods to effectively reduce the gene dimensionality, retain the key epistatic effects, and effectively characterize the relationship between epistatic effects and complex diseases.
复杂疾病不同于孟德尔疾病。其发展通常涉及多个基因的相互作用或基因与环境之间的相互作用（即上位性）。尽管针对复杂疾病的高通量测序技术已经产生了大量数据，但由于特征维度高以及上位性分析中的组合问题，对这些数据进行分析极其困难。在这项工作中，我们引入机器学习方法来有效降低基因维度，保留关键的上位性效应，并有效表征上位性效应与复杂疾病之间的关系。
