ELM-MHC: An Improved MHC Identification Method with Extreme Learning Machine Algorithm
ELM-MHC: An Improved MHC Identification Method with Extreme Learning Machine Algorithm
复制标题
ELM-MHC：一种基于极限学习机算法的改进MHC识别方法
DOI：
10.1021/acs.jproteome.9b00012
复制
发表时间：
2019-03-01
影响因子：
4.4
通讯作者：
Zou, Quan
中科院分区：
文献类型：
--
作者：
Li, Yanjuan;Niu, Mengting;Zou, Quan
主要组织相容性复合体（MHC）是主要组织相容性抗原的所有基因组的术语。它与来自病原体的肽链结合，并将病原体展示在细胞表面，以促进T细胞识别并执行一系列免疫功能。MHC分子在移植、自身免疫、感染和肿瘤免疫治疗中至关重要。结合机器学习算法，充分利用生物信息学分析技术，更准确地识别MHC是一项重要的任务。与传统的生物学方法相比，本文提出了一种新的MHC识别方法，并利用所建立的分类器对MHC I和MHC II进行了分类识别。该分类器采用SVMProt 188 D、Bag-of-ngrams（BonG）和信息论（IT）混合特征表示方法，使用极限学习机（ELM），选择linkernel作为激活函数，采用10折交叉验证和独立测试集验证来验证所构建分类器的准确性，同时识别MHC和识别MHC I和MHC II，分别通过10折交叉验证，该算法在识别MHC时获得了91.66%的准确率，在识别MHC类别时获得了94.442%的准确率。此外，用以下URL构建了名为ELM-MHC的在线鉴定网站：http://server.malab.cn/ELM-MHC/。
The major histocompatibility complex (MHC) is a term for all gene groups of a major histocompatibility antigen. It binds to peptide chains derived from pathogens and displays pathogens on the cell surface to facilitate T-cell recognition and perform a series of immune functions. MHC molecules are critical in transplantation, autoimmunity, infection, and tumor immunotherapy. Combining machine learning algorithms and making full use of bioinformatics analysis technology, more accurate recognition of MHC is an important task. The paper proposed a new MHC recognition method compared with traditional biological methods and used the built classifier to classify and identify MHC I and MHC II. The classifier used the SVMProt 188D, bag-of-ngrams (BonG), and information theory (IT) mixed feature representation methods and used the extreme learning machine (ELM), which selects linkernel as the activation function and used 10-fold cross-validation and the independent test set validation to verify the accuracy of the constructed classifier and simultaneously identify the MHC and identify the MHC I and MHC II, respectively. Through the 10-fold cross-validation, the proposed algorithm obtained 91.66% accuracy when identifying MHC and 94.442% accuracy when identifying MHC categories. Furthermore, an online identification Web site named ELM-MHC was constructed with the following URL: http://server.malab.cn/ELM-MHC/.