Semi-supervised learning for potential human microRNA-disease associations inference.
Semi-supervised learning for potential human microRNA-disease associations inference.
复制标题
用于潜在人类 microRNA 疾病关联推理的半监督学习
DOI：
10.1038/srep05501
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发表时间：
2014-06-30
影响因子：
4.6
通讯作者：
Yan GY
中科院分区：
文献类型：
--
作者：
Chen X;Yan GY
MicroRNAs在各种疾病的发生和发展中起着至关重要的作用。从大量的生物学数据中预测潜在的mirna与疾病的关联是生物医学研究中的一个重要问题。考虑到以往方法的局限性，我们开发了mirna -疾病关联的正则化最小二乘法（RLSMDA）来揭示疾病与mirna之间的关系。RLSMDA可以对没有已知相关mirna的疾病起作用。此外，它是一种半监督（不需要阴性样本）和全局方法（同时优先考虑所有疾病的关联）。基于留一交叉验证，可靠的AUC证明了RLSMDA的可靠性能。我们还将RLSMDA应用于肝细胞癌和肺癌，同时实现了所有疾病的全局预测。因此，基于全球预测的前50个预测mirna中的80%（肝细胞癌）和84%（肺癌）以及前20个潜在关联中的75%已被生物学实验证实。我们还将RLSMDA应用于金标准数据集中没有已知相关mirna的疾病。结果，在RLSMDA预测的32种疾病的前3位潜在相关miRNA列表中，34种疾病-miRNA关联通过实验成功确认。预计RLSMDA将成为生物医学研究的一个有用的生物信息学资源。
MicroRNAs play critical role in the development and progression of various diseases. Predicting potential miRNA-disease associations from vast amount of biological data is an important problem in the biomedical research. Considering the limitations in previous methods, we developed Regularized Least Squares for MiRNA-Disease Association (RLSMDA) to uncover the relationship between diseases and miRNAs. RLSMDA can work for diseases without known related miRNAs. Furthermore, it is a semi-supervised (does not need negative samples) and global method (prioritize associations for all the diseases simultaneously). Based on leave-one-out cross validation, reliable AUC have demonstrated the reliable performance of RLSMDA. We also applied RLSMDA to Hepatocellular cancer and Lung cancer and implemented global prediction for all the diseases simultaneously. As a result, 80% (Hepatocellular cancer) and 84% (Lung cancer) of top 50 predicted miRNAs and 75% of top 20 potential associations based on global prediction have been confirmed by biological experiments. We also applied RLSMDA to diseases without known related miRNAs in golden standard dataset. As a result, in the top 3 potential related miRNA list predicted by RLSMDA for 32 diseases, 34 disease-miRNA associations were successfully confirmed by experiments. It is anticipated that RLSMDA would be a useful bioinformatics resource for biomedical researches.
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影响因子：
3.3
作者：
Danielsson, K.;Wahlin, Y. B.;Nylander, K.
通讯作者：
Nylander, K.
影响因子：
37.3
作者：
Dong P;Kaneuchi M;Watari H;Hamada J;Sudo S;Ju J;Sakuragi N
通讯作者：
Sakuragi N
影响因子：
37.3
作者：
Cahill S;Smyth P;Denning K;Flavin R;Li J;Potratz A;Guenther SM;Henfrey R;O'Leary JJ;Sheils O
通讯作者：
Sheils O
DOI：
10.1186/1758-907x-1-6
发表时间：
2010-02-02
期刊：
Silence
影响因子：
--
作者：
Bandyopadhyay S;Mitra R;Maulik U;Zhang MQ
通讯作者：
Zhang MQ
影响因子：
2.7
作者：
Chen H;Zhang Z
通讯作者：
Zhang Z