发起人
计算机科学
人工智能
机器学习
分类器(UML)
人工神经网络
感知器
抄写(语言学)
计算生物学
基因
模式识别(心理学)
生物
遗传学
基因表达
语言学
哲学
作者
Zuber Khan,Rajdeep,Ravi Kumar Arya,Tirupathiraju Kanumuri
标识
DOI:10.1109/indicon49873.2020.9342360
摘要
The quest to extract vital information regarding protein-coding genes has always fascinated scientific community. However, there is scarcity of experimental data on internal mechanism controlling the synthesis of functional gene product. This is due to the fact that the process of identification of transcription site is highly complex. In order to understand transcription, it is essential to identify promoters since promoter sequences define where transcription of a gene begins. The use of Machine Learning has provided substantially accurate results as compared to conventional methods. This research paper aims at classification of short E. Coli DNA Sequences into Promoter and Non- Promoter category using machine learning algorithms like AdaBoost Classifier and Multilayer Perceptron Neural Network with a higher accuracy than existing methodologies. It also compares the accuracy of algorithms such as Support Vector Classifier (`RBF' and `Sigmoid' Kernel) and Gaussian Process Classifier that were not used before in promoter identification.
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