Identification of real microRNA precursors with a pseudo structure status composition approach.
Identification of real microRNA precursors with a pseudo structure status composition approach.
复制标题
用伪结构状态组成方法鉴定真正的 MicroRNA 前体
DOI：
10.1371/journal.pone.0121501
复制
发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
Chou KC
中科院分区：
文献类型：
--
作者：
Liu B;Fang L;Liu F;Wang X;Chen J;Chou KC
包含大约22个核RNA（缩写的miRNA）是一个小的非编码RNA分子，在基因表达的转录和转录后调节中起作用。在许多癌症和其他疾病状态中都观察到了生物过程。通过这些疾病，尤其是在癌变中RNA序列的雪崩在后基因组时代产生的，高度希望在这方面开发基于计算的方法。提出了“ IMCRNA-PSESSC”和“ IMCRNA-EXPSESSC”，用于使用与伪氨基酸组成方法非常相似的方式来识别人类前薄膜在更大，更严格的新构建的基准数据集中，显示了两个新预测因子（可在http://bioinformatics.hitsz.edu.cn/imcrna/访问）均优胜于或胜过我们与该领域最好的现有预测指标相当。
Containing about 22 nucleotides, a micro RNA (abbreviated miRNA) is a small non-coding RNA molecule, functioning in transcriptional and post-transcriptional regulation of gene expression. The human genome may encode over 1000 miRNAs. Albeit poorly characterized, miRNAs are widely deemed as important regulators of biological processes. Aberrant expression of miRNAs has been observed in many cancers and other disease states, indicating they are deeply implicated with these diseases, particularly in carcinogenesis. Therefore, it is important for both basic research and miRNA-based therapy to discriminate the real pre-miRNAs from the false ones (such as hairpin sequences with similar stem-loops). Particularly, with the avalanche of RNA sequences generated in the postgenomic age, it is highly desired to develop computational sequence-based methods in this regard. Here two new predictors, called “iMcRNA-PseSSC” and “iMcRNA-ExPseSSC”, were proposed for identifying the human pre-microRNAs by incorporating the global or long-range structure-order information using a way quite similar to the pseudo amino acid composition approach. Rigorous cross-validations on a much larger and more stringent newly constructed benchmark dataset showed that the two new predictors (accessible at http://bioinformatics.hitsz.edu.cn/iMcRNA/) outperformed or were highly comparable with the best existing predictors in this area.
登录
查看更多内容
影响因子：
4.1
作者：
CHOU, KC;FORSEN, S
通讯作者：
FORSEN, S
影响因子：
5.8
作者：
Chou, KC
通讯作者：
Chou, KC
影响因子：
4.5
作者：
Cai, XZ;Hagedorn, CH;Cullen, BR
通讯作者：
Cullen, BR
影响因子：
2
作者：
Chen, Yen-Kuang;Li, Kuo-Bin
通讯作者：
Li, Kuo-Bin
PseKNC-General：用于生成各种模式的假核苷酸组合物的跨平台包
影响因子：
5.8
作者：
Chen, Wei;Zhang, Xitong;Chou, Kuo-Chen
通讯作者：
Chou, Kuo-Chen