典型文献
Towards a better prediction of subcellular location of long non-coding RNA
文献摘要：
The spatial distribution pattern of long non-coding RNA(lncRNA)in cell is tightly related to their function.With the increment of publicly available subcellular location data,a number of computational methods have been developed for the recognition of the subcellular localization of lncRNA.Unfor-tunately,these computational methods suffer from the low discriminative power of redundant features or overfitting of oversampling.To address those issues and enhance the predic-tion performance,we present a support vector machine-based approach by incorporating mutual information algorithm and incremental feature selection strategy.As a result,the new predictor could achieve the overall accuracy of 91.60％.The highly automated web-tool is available at lin-group.cn/server/iLoc-LncRNA(2.0)/website.It will help to get the knowledge of lncRNA subcellular localization.
文献关键词：
中图分类号：
作者姓名：
Zhao-Yue ZHANG;Zi-Jie SUN;Yu-He YANG;Hao LIN
作者机构：
Key Laboratory for NeuroInformation of Ministry of Education,School of Life Science and Technology and Center for Informational Biology,University of Electronic Science and Technology of China,Chengdu 610054,China
文献出处：
引用格式：
[1]Zhao-Yue ZHANG;Zi-Jie SUN;Yu-He YANG;Hao LIN-.Towards a better prediction of subcellular location of long non-coding RNA)[J].计算机科学前沿,2022(05):194-200
A类：
iLoc
B类：
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AB值：
0.650656
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