AntiDMPpred: a web service for identifying anti-diabetic peptides.
AntiDMPpred: a web service for identifying anti-diabetic peptides.
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AntiDMPpred：用于识别抗糖尿病肽的网络服务
作者：
Chen, Xue;Huang, Jian;He, Bifang
糖尿病（Diabetes mellitus，DM）是一种慢性代谢性疾病，已成为全球范围内人类健康的主要威胁，给人类带来了巨大的经济和社会危害。口服抗糖尿病肽类药物已成为糖尿病治疗的新途径。许多生物活性肽已被证明具有潜在的抗糖尿病特性，并有望作为预防和管理糖尿病的替代治疗措施。抗糖尿病肽的计算预测有助于在寻找新的有效的治疗糖尿病的肽药物的过程中促进基于肽的药物发现。在这里，我们采用随机森林开发了一个计算模型，名为AntiDMPpred，用于预测抗糖尿病肽。首先构建了具有236种抗糖尿病肽和236种非抗糖尿病肽的基准数据集。四种类型的序列衍生的描述符被用来代表肽序列。然后，我们结合了四种机器学习方法和六种特征评分方法来选择非冗余特征，这些特征被输入到不同的机器学习分类器中来训练模型。实验结果表明，AntiDMPpred在嵌套五重交叉验证中达到了77.12%的准确率和0.8193的受试者工作曲线下面积（AUCROC），产生了令人满意的性能，超过了本研究中实现的其他分类器。该网络服务可在http://i.uestc.edu.cn/AntiDMPpred/cgi-bin/AntiDMPpred.pl上免费访问。我们希望AntiDMPpred能够促进抗糖尿病活性肽的发现。
Diabetes mellitus (DM) is a chronic metabolic disease that has been a major threat to human health globally, causing great economic and social adversities. The oral administration of anti-diabetic peptide drugs has become a novel route for diabetes therapy. Numerous bioactive peptides have demonstrated potential anti-diabetic properties and are promising as alternative treatment measures to prevent and manage diabetes. The computational prediction of anti-diabetic peptides can help promote peptide-based drug discovery in the process of searching newly effective therapeutic peptide agents for diabetes treatment. Here, we resorted to random forest to develop a computational model, named AntiDMPpred, for predicting anti-diabetic peptides. A benchmark dataset with 236 anti-diabetic and 236 non-anti-diabetic peptides was first constructed. Four types of sequence-derived descriptors were used to represent the peptide sequences. We then combined four machine learning methods and six feature scoring methods to select the non-redundant features, which were fed into diverse machine learning classifiers to train the models. Experimental results show that AntiDMPpred reached an accuracy of 77.12% and area under the receiver operating curve (AUCROC) of 0.8193 in the nested five-fold cross-validation, yielding a satisfactory performance and surpassing other classifiers implemented in the study. The web service is freely accessible at http://i.uestc.edu.cn/AntiDMPpred/cgi-bin/AntiDMPpred.pl. We hope AntiDMPpred could improve the discovery of anti-diabetic bioactive peptides.
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影响因子：
23.8
作者：
Chu, Yanyi;Zhang, Yan;Wei, Dong-Qing
通讯作者：
Wei, Dong-Qing
DOI：
10.1093/database/bas015
发表时间：
2012
期刊：
Database : the journal of biological databases and curation
影响因子：
--
作者：
Gautam A;Singh H;Tyagi A;Chaudhary K;Kumar R;Kapoor P;Raghava GP
通讯作者：
Raghava GP
DOI：
10.1093/bioinformatics/bts565
发表时间：
2012-12-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Fu L;Niu B;Zhu Z;Wu S;Li W
通讯作者：
Li W
影响因子：
4.4
作者：
Charoenkwan, Phasit;Kanthawong, Sakawrat;Shoombuatong, Watshara
通讯作者：
Shoombuatong, Watshara
影响因子：
4.6
作者：
Chowdhury AS;Reehl SM;Kehn-Hall K;Bishop B;Webb-Robertson BM
通讯作者：
Webb-Robertson BM