Recently unique spans of genetic data are produced by researchers, there is a trend in genetic exploration using machine learning integrated analysis and virtual combination of adaptive data into the solution of classification problems. Detection of ailments and infections at early stage is of key concern and a huge challenge for researchers in the field of machine learning classification and bioinformatics. Considerate genes contributing to diseases are of huge dispute to a lot of researchers. This study reviews various works on Dimensionality reduction techniques for reducing sets of features that groups data effectively with less computational processing time and classification methods that contributes to the advances of RNA-Sequencing approach.

最近，研究人员产生了独特的遗传数据范围，在遗传探索中，使用机器学习集成分析和将自适应数据虚拟组合到分类问题的解决方案中存在一种趋势。在机器学习分类和生物信息学领域，对疾病和感染的早期检测是研究人员关注的重大问题，也是巨大的挑战。引起疾病的体贴基因对许多研究人员来说是巨大的争议。这项研究回顾了关于降维技术的各种工作，这些技术可减少以较少的计算处理时间有效地对数据进行分组的特征集和有助于RNA测序方法发展的分类方法。