期刊
BIOINFORMATICS
卷 35, 期 23, 页码 4930-4937出版社
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btz408
关键词
-
类别
资金
- National Natural Science Foundation of China [61701340, 61702361, 61771331, 61772376, 61572213]
- Natural Science Foundation of Tianjin city [18JCQNJC00500, 18JCQNJC00800]
- National Key R&D Program of China [2018YFC0910405]
- Basic Science Research Program through the National Research Foundation of Korea - Ministry of Education, Science, and Technology [2018R1D1A1B07049572]
- Natural Science Foundation of Fujian Province of China [2016J01152]
- National Research Foundation of Korea [2018R1D1A1B07049572] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
Motivation: Accurate identification of N4-methylcytosine (4mC) modifications in a genome wide can provide insights into their biological functions and mechanisms. Machine learning recently have become effective approaches for computational identification of 4mC sites in genome. Unfortunately, existing methods cannot achieve satisfactory performance, owing to the lack of effective DNA feature representations that are capable to capture the characteristics of 4mC modifications. Results: In this work, we developed a new predictor named 4mcPred-IFL, aiming to identify 4mC sites. To represent and capture discriminative features, we proposed an iterative feature representation algorithm that enables to learn informative features from several sequential models in a supervised iterative mode. Our analysis results showed that the feature representations learnt by our algorithm can capture the discriminative distribution characteristics between 4mC sites and non-4mC sites, enlarging the decision margin between the positives and negatives in feature space. Additionally, by evaluating and comparing our predictor with the state-of-the-art predictors on benchmark datasets, we demonstrate that our predictor can identify 4mC sites more accurately.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据