MultiFeatVotPIP:预测促炎肽的基于投票的集合学习框架。

IF 6.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Briefings in bioinformatics Pub Date : 2024-09-23 DOI:10.1093/bib/bbae505
Chaorui Yan, Aoyun Geng, Zhuoyu Pan, Zilong Zhang, Feifei Cui
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引用次数: 0

摘要

炎症反应可能导致组织或器官损伤,而促炎肽(PIPs)是能够诱发这种反应的信号肽。许多疾病已被重新定义为炎症性疾病。为了更有效地识别 PIPs,我们扩大了数据集,并设计了一个具有人工编码特征的集合学习模型。具体来说,我们采用了一种更全面的特征编码方法,并考虑了某些特征的实际影响,对其进行了过滤。我们使用基于五个不同分类器的集合学习模型对 PIP 进行了识别和预测。结果表明,该模型的灵敏度、特异性、准确度和马修斯相关系数均高于最先进的模型。我们将该模型命名为 MultiFeatVotPIP,模型和数据均可在 https://github.com/ChaoruiYan019/MultiFeatVotPIP 上公开访问。此外,我们还为用户开发了一个友好的网络界面,访问网址为 http://www.bioai-lab.com/MultiFeatVotPIP。
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MultiFeatVotPIP: a voting-based ensemble learning framework for predicting proinflammatory peptides.

Inflammatory responses may lead to tissue or organ damage, and proinflammatory peptides (PIPs) are signaling peptides that can induce such responses. Many diseases have been redefined as inflammatory diseases. To identify PIPs more efficiently, we expanded the dataset and designed an ensemble learning model with manually encoded features. Specifically, we adopted a more comprehensive feature encoding method and considered the actual impact of certain features to filter them. Identification and prediction of PIPs were performed using an ensemble learning model based on five different classifiers. The results show that the model's sensitivity, specificity, accuracy, and Matthews correlation coefficient are all higher than those of the state-of-the-art models. We named this model MultiFeatVotPIP, and both the model and the data can be accessed publicly at https://github.com/ChaoruiYan019/MultiFeatVotPIP. Additionally, we have developed a user-friendly web interface for users, which can be accessed at http://www.bioai-lab.com/MultiFeatVotPIP.

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来源期刊
Briefings in bioinformatics
Briefings in bioinformatics 生物-生化研究方法
CiteScore
13.20
自引率
13.70%
发文量
549
审稿时长
6 months
期刊介绍: Briefings in Bioinformatics is an international journal serving as a platform for researchers and educators in the life sciences. It also appeals to mathematicians, statisticians, and computer scientists applying their expertise to biological challenges. The journal focuses on reviews tailored for users of databases and analytical tools in contemporary genetics, molecular and systems biology. It stands out by offering practical assistance and guidance to non-specialists in computerized methodologies. Covering a wide range from introductory concepts to specific protocols and analyses, the papers address bacterial, plant, fungal, animal, and human data. The journal's detailed subject areas include genetic studies of phenotypes and genotypes, mapping, DNA sequencing, expression profiling, gene expression studies, microarrays, alignment methods, protein profiles and HMMs, lipids, metabolic and signaling pathways, structure determination and function prediction, phylogenetic studies, and education and training.
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