RNA序列
计算机科学
机器学习
人工智能
核糖核酸
计算生物学
转录组
数据挖掘
生物信息学
基因
基因表达
生物
遗传学
作者
Almas Jabeen,Nadeem Ahmad,Khalid Raza
出处
期刊:Lecture notes in computational vision and biomechanics
日期:2017-11-14
卷期号:: 133-172
被引量:24
标识
DOI:10.1007/978-3-319-65981-7_6
摘要
Ribonucleic acid sequencing (RNA-Seq) measures the expression levels of several transcripts simultaneously. The readings can be gene, exon, or other regions of interest. Various computational tools have been developed for studying pathogens or viruses from RNA-Seq data by classifying them according to the attributes in several pre-defined classes. However, computational tools and approaches to analyzing complex datasets are still lacking. The development of classification models is highly recommended for the diagnosis and classification of diseases, disease monitoring at the molecular level and research into potential disease biomarkers. In this chapter, we discuss various machine learning approaches for RNA-Seq data classification and their implementation. These advancements in bioinformatics, along with developments in machine learning-based classification, would provide powerful toolboxes for the classification of transcriptome information available through RNA-Seq data.
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