{"title":"预测NLR蛋白的机器学习技术比较","authors":"Nadia, Ekta Gandotra, Narendra Kumar","doi":"10.4015/s1016237222500508","DOIUrl":null,"url":null,"abstract":"The nucleotide-binding domain leucine-rich repeat-containing (NLR) proteins plays significant role in the intestinal tissue repair and innate immunity. It recently added to the members of innate immunity effectors molecules. It also plays an essential role in intestinal microbiota and recently emerged as a crucial hit for developing ulcerative colitis (UC) and colitis-associated cancer (CAC). A machine learning-based approach for predicting NLR proteins has been developed. In this study, we present a comparison of three supervised machine learning algorithms. Using ProtR and POSSUM Packages, the features are extracted for the dataset used in this work. The models are trained with the input compositional features generated using dipeptide composition, amino acid composition, etc., as well as Position Specific Scoring Matrix (PSSM) based compositions. The dataset consists of 390 proteins for the negative and positive datasets. The five-fold cross-validation (CV) is used to optimize Sequential Minimal Optimization (SMO) library of Support Vector Machine (LIBSVM) and Random Forest (RF) parameters, and the best model was selected. The proposed work performs rationally well with an accuracy of 90.91% and 93.94% for RF as the best classifier for the Amino Acid Composition (AAC) and PSE_PSSM-based model. We believe that this method is a reliable, rapid and useful prediction method for NLR Protein.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"1 1","pages":""},"PeriodicalIF":0.6000,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"COMPARISON OF MACHINE LEARNING TECHNIQUES FOR PREDICTING NLR PROTEINS\",\"authors\":\"Nadia, Ekta Gandotra, Narendra Kumar\",\"doi\":\"10.4015/s1016237222500508\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The nucleotide-binding domain leucine-rich repeat-containing (NLR) proteins plays significant role in the intestinal tissue repair and innate immunity. It recently added to the members of innate immunity effectors molecules. It also plays an essential role in intestinal microbiota and recently emerged as a crucial hit for developing ulcerative colitis (UC) and colitis-associated cancer (CAC). A machine learning-based approach for predicting NLR proteins has been developed. In this study, we present a comparison of three supervised machine learning algorithms. Using ProtR and POSSUM Packages, the features are extracted for the dataset used in this work. The models are trained with the input compositional features generated using dipeptide composition, amino acid composition, etc., as well as Position Specific Scoring Matrix (PSSM) based compositions. The dataset consists of 390 proteins for the negative and positive datasets. The five-fold cross-validation (CV) is used to optimize Sequential Minimal Optimization (SMO) library of Support Vector Machine (LIBSVM) and Random Forest (RF) parameters, and the best model was selected. The proposed work performs rationally well with an accuracy of 90.91% and 93.94% for RF as the best classifier for the Amino Acid Composition (AAC) and PSE_PSSM-based model. We believe that this method is a reliable, rapid and useful prediction method for NLR Protein.\",\"PeriodicalId\":8862,\"journal\":{\"name\":\"Biomedical Engineering: Applications, Basis and Communications\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":0.6000,\"publicationDate\":\"2022-11-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biomedical Engineering: Applications, Basis and Communications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4015/s1016237222500508\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biomedical Engineering: Applications, Basis and Communications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4015/s1016237222500508","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0
摘要
核苷酸结合域富含亮氨酸重复序列(NLR)蛋白在肠道组织修复和先天免疫中起重要作用。它最近加入了先天免疫效应分子的成员。它在肠道微生物群中也起着至关重要的作用,最近被发现是溃疡性结肠炎(UC)和结肠炎相关癌症(CAC)的关键打击。一种基于机器学习的预测NLR蛋白的方法已经被开发出来。在这项研究中,我们提出了三种监督机器学习算法的比较。使用ProtR和POSSUM包,为本工作中使用的数据集提取特征。使用二肽组成、氨基酸组成等生成的输入组成特征以及基于位置特定评分矩阵(Position Specific Scoring Matrix, PSSM)的组合来训练模型。该数据集由390个蛋白质组成,分别用于阴性和阳性数据集。采用五重交叉验证(CV)对支持向量机(LIBSVM)和随机森林(RF)参数的序贯最小优化(SMO)库进行优化,选出最优模型。结果表明,RF作为氨基酸组成(AAC)和pse_pssm模型的最佳分类器,准确率分别为90.91%和93.94%。该方法是一种可靠、快速、实用的NLR蛋白预测方法。
COMPARISON OF MACHINE LEARNING TECHNIQUES FOR PREDICTING NLR PROTEINS
The nucleotide-binding domain leucine-rich repeat-containing (NLR) proteins plays significant role in the intestinal tissue repair and innate immunity. It recently added to the members of innate immunity effectors molecules. It also plays an essential role in intestinal microbiota and recently emerged as a crucial hit for developing ulcerative colitis (UC) and colitis-associated cancer (CAC). A machine learning-based approach for predicting NLR proteins has been developed. In this study, we present a comparison of three supervised machine learning algorithms. Using ProtR and POSSUM Packages, the features are extracted for the dataset used in this work. The models are trained with the input compositional features generated using dipeptide composition, amino acid composition, etc., as well as Position Specific Scoring Matrix (PSSM) based compositions. The dataset consists of 390 proteins for the negative and positive datasets. The five-fold cross-validation (CV) is used to optimize Sequential Minimal Optimization (SMO) library of Support Vector Machine (LIBSVM) and Random Forest (RF) parameters, and the best model was selected. The proposed work performs rationally well with an accuracy of 90.91% and 93.94% for RF as the best classifier for the Amino Acid Composition (AAC) and PSE_PSSM-based model. We believe that this method is a reliable, rapid and useful prediction method for NLR Protein.
期刊介绍:
Biomedical Engineering: Applications, Basis and Communications is an international, interdisciplinary journal aiming at publishing up-to-date contributions on original clinical and basic research in the biomedical engineering. Research of biomedical engineering has grown tremendously in the past few decades. Meanwhile, several outstanding journals in the field have emerged, with different emphases and objectives. We hope this journal will serve as a new forum for both scientists and clinicians to share their ideas and the results of their studies.
Biomedical Engineering: Applications, Basis and Communications explores all facets of biomedical engineering, with emphasis on both the clinical and scientific aspects of the study. It covers the fields of bioelectronics, biomaterials, biomechanics, bioinformatics, nano-biological sciences and clinical engineering. The journal fulfils this aim by publishing regular research / clinical articles, short communications, technical notes and review papers. Papers from both basic research and clinical investigations will be considered.