清晨好,您是今天最早来到科研通的研友!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您科研之路漫漫前行!

Computational Prediction of Human Disease- Associated circRNAs Based on Manifold Regularization Learning Framework

计算机科学 人工智能 正规化(语言学) 机器学习
作者
Qiu Xiao,Jiawei Luo,Jianhua Dai
出处
期刊:IEEE Journal of Biomedical and Health Informatics [Institute of Electrical and Electronics Engineers]
卷期号:23 (6): 2661-2669 被引量:72
标识
DOI:10.1109/jbhi.2019.2891779
摘要

The accumulating evidences regarding circular RNAs (circRNAs) indicate that they play crucial roles in a wide range of biological processes and participate in tumorigenesis and progression. The number of newly discovered circRNAs have increased dramatically in recent years, but the functions of vast majority of circRNAs remain unknown, and little effort has been devoted to discover disease-associated circRNAs on a large scale until now. With the advancement of high-throughput technology, the increasing availability of omics data has provided an unprecedented opportunity for prioritizing candidate circRNAs for diseases by computational models, which will contribute to exploring the pathogenesis of complex diseases at the circRNA level and provide promising applications in disease diagnosis and treatment. Here we propose the assumption that circRNAs with similar functions are normally associated with similar diseases and vice versa, and develop an integrated computational framework called MRLDC to identify disease-associated circRNAs. To our knowledge, little efforts have been developed for uncovering circRNA-disease associations on a large scale. By fully exploiting the experimentally validated associations between diseases and circRNAs, we first compute the Gaussian interaction profile kernel similarity for circRNAs and diseases, and then a heterogeneous circRNA-disease bilayer network is constructed by combining a circRNA similar network, a disease similar network, and known circRNA-disease associations. Subsequently, we develop a weighted low-rank approximation optimization algorithm with dual-manifold regularizations for predicting disease-associated circRNAs. Experimental results indicate that MRLDC can effectively identify disease circRNA candidates with high accuracy. In addition, case studies further demonstrate the ability of our method in discovering potential circRNA-disease associations.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
3秒前
小小蚂蚁发布了新的文献求助10
9秒前
i3utter完成签到,获得积分10
13秒前
醉清风完成签到 ,获得积分10
17秒前
科研通AI2S应助科研通管家采纳,获得10
46秒前
ommphey完成签到 ,获得积分10
49秒前
可可完成签到 ,获得积分10
56秒前
浮生完成签到,获得积分10
1分钟前
行云流水完成签到,获得积分10
1分钟前
达进发布了新的文献求助10
1分钟前
tangchao完成签到,获得积分10
1分钟前
CSPC001完成签到 ,获得积分10
1分钟前
小小蚂蚁完成签到,获得积分10
1分钟前
一一一完成签到,获得积分10
1分钟前
MM完成签到 ,获得积分10
1分钟前
fearlessji完成签到 ,获得积分10
1分钟前
00完成签到 ,获得积分10
1分钟前
封闭货车完成签到 ,获得积分10
2分钟前
2分钟前
linakg发布了新的文献求助10
2分钟前
Woodenman完成签到 ,获得积分0
2分钟前
linakg完成签到,获得积分10
2分钟前
失眠的香蕉完成签到 ,获得积分10
2分钟前
清风完成签到 ,获得积分10
2分钟前
领导范儿应助hongtao采纳,获得10
3分钟前
淡然的芷荷完成签到 ,获得积分10
3分钟前
xingxingwang完成签到,获得积分10
3分钟前
3分钟前
合适的寄灵完成签到 ,获得积分10
3分钟前
xixihaha完成签到,获得积分10
3分钟前
南宫清涟发布了新的文献求助10
3分钟前
w婷完成签到 ,获得积分10
3分钟前
一颗红葡萄完成签到 ,获得积分10
3分钟前
南宫清涟完成签到,获得积分10
4分钟前
swordshine完成签到,获得积分10
4分钟前
4分钟前
ceeray23发布了新的文献求助20
4分钟前
lani完成签到 ,获得积分10
4分钟前
失眠的笑翠完成签到 ,获得积分10
4分钟前
诺贝尔候选人完成签到 ,获得积分10
4分钟前
高分求助中
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3990835
求助须知:如何正确求助?哪些是违规求助? 3532241
关于积分的说明 11256614
捐赠科研通 3271100
什么是DOI,文献DOI怎么找? 1805229
邀请新用户注册赠送积分活动 882302
科研通“疑难数据库(出版商)”最低求助积分说明 809236