【摘要】 随着人类基因组计划的顺利实施，生物分子数据应运而生并呈爆炸式增长，再加上互联网信息技术作为强有力的数据处理工具的快速革新与普及，共同推动了生物信息学的繁荣发展.面对海量的实验数据，仅使用生物实验的方法去处理以探究其中的有效信息需耗费大量的人力物力，成本很高，所以研究者们试图应用并不断开发计算的方法.由于近年来机器学习技术的有效性不断得到验证，采用机器学习方法进行生物医学数据分析成为一个新的研究范畴.文章主要介绍了两种生物医学数据研究方法，即基于生物大分子序列的分析方法和基于向量或矩阵数据的分析方法.更多还原

【Abstract】 The Human Genome Project has been successfully implemented, which has resulted in huge amounts of biomolecular data. Coupled with the rapid development of the Internet, bioinformatics has developed rapidly in recent years. With the rapid growth of biomolecular data, researchers in biomolecular and related fields have rapidly obtained many experimental data. However, using biological experimental methods to study these data is time-consuming and costly. As the effectiveness of machine learning technology has been continuously verified in recent years, the use of machine learning methods for biomedical data analysis has become a new research area. This article mainly introduces two research methods of biomedical data, analysis methods based on biological macromolecular sequences and analysis methods based on vector or matrix data.更多还原