METSP: a maximum-entropy classifier based text mining tool for transporter-substrate identification with semistructured text.
METSP: a maximum-entropy classifier based text mining tool for transporter-substrate identification with semistructured text.
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METSP：基于最大熵分类器的文本挖掘工具，用于半结构化文本的转运蛋白底物识别
DOI：
10.1155/2015/254838
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发表时间：
2015
影响因子：
--
通讯作者：
Qu H
中科院分区：
文献类型：
--
作者：
Zhao M;Chen Y;Qu D;Qu H
转运体的底物不仅有助于推断转运体的功能，而且对发现化合物-化合物相互作用和重建代谢途径也很重要。尽管随着体外转运蛋白测定等新技术的发展，已经积累了大量的数据，但对转运蛋白底物的寻找还远远没有完成。在本文中，我们介绍了METSP，一个最大熵分类器，致力于从半结构化文本中检索转运体-底物对（tsp）。基于UniProt的高质量标注，METSP在交叉验证实验中达到了较高的准确率和召回率。将METSP应用于UniProt的182829个人类转运蛋白注释句子中，识别出3942个包含转运蛋白和复合信息的句子。最后，1547个机密的人类tsp被确定为进一步的人工管理，其中58.37%的新底物未在公共运输数据库中注释。METSP是UniProt中第一个从半结构化注释文本中提取tsp的有效工具。该工具可以帮助确定转运体的精确底物和药物，从而促进药物靶标预测、代谢网络重建和文献分类。
The substrates of a transporter are not only useful for inferring function of the transporter, but also important to discover compound-compound interaction and to reconstruct metabolic pathway. Though plenty of data has been accumulated with the developing of new technologies such as in vitro transporter assays, the search for substrates of transporters is far from complete. In this article, we introduce METSP, a maximum-entropy classifier devoted to retrieve transporter-substrate pairs (TSPs) from semistructured text. Based on the high quality annotation from UniProt, METSP achieves high precision and recall in cross-validation experiments. When METSP is applied to 182,829 human transporter annotation sentences in UniProt, it identifies 3942 sentences with transporter and compound information. Finally, 1547 confidential human TSPs are identified for further manual curation, among which 58.37% pairs with novel substrates not annotated in public transporter databases. METSP is the first efficient tool to extract TSPs from semistructured annotation text in UniProt. This tool can help to determine the precise substrates and drugs of transporters, thus facilitating drug-target prediction, metabolic network reconstruction, and literature classification.