Proteomics is the extensive investigation of proteins which has empowered the recognizable proof of consistently expanding quantities of protein. Proteins are necessary part of living life form, with numerous capacities. The proteome is the complete arrangement of proteins that are created or altered by a life form or framework of the organism. Proteome fluctuates with time and unambiguous prerequisites, or stresses, that a cell or organism experiences. Proteomics is an interdisciplinary area that has derived from the hereditary data of different genome ventures. Much proteomics information is gathered with the assistance of high throughput techniques, for example, mass spectrometry and microarray. It would regularly take weeks or months to analyze the information and perform examinations by hand. Therefore, scholars and scientific experts are teaming up with computer science researchers and mathematicians to make projects and pipeline to computationally examine the protein information. Utilizing bioinformatics procedures, scientists are prepared to do quicker investigation and protein information storing. The goal of this paper is to brief about the review of machine learning procedures and its application in the field of proteomics.

蛋白质组学是对蛋白质的广泛研究，它为不断增加蛋白质数量提供了可识别的证据。蛋白质是生命形式的必要组成部分，具有多种能力。蛋白质组是由生物体的生命形式或框架产生或改变的蛋白质的完整排列。蛋白质组随着时间和细胞或有机体经历的明确先决条件或压力而波动。蛋白质组学是一个跨学科领域，它源自不同基因组企业的遗传数据。许多蛋白质组学信息是在高通量技术的帮助下收集的，例如质谱和微阵列。通常需要数周或数月的时间来分析信息并手动进行检查。所以，学者和科学专家正在与计算机科学研究人员和数学家合作，制定项目和管道以计算检查蛋白质信息。利用生物信息学程序，科学家准备进行更快的调查和蛋白质信息存储。本文的目的是简要介绍机器学习程序的回顾及其在蛋白质组学领域的应用。