In recent era prediction of enzyme class from an unknown protein is one of the challenging tasks in bioinformatics. Day to day the number of proteins is increases as result the prediction of enzyme class gives a new opportunity to bioinformatics scholars. The prime objective of this article is to implement the machine learning classification technique for feature selection and predictions also find out an appropriate classification technique for function prediction. In this article the seven different classification technique like CRT, QUEST, CHAID, C5.0, ANN (Artificial Neural Network), SVM and Bayesian has been implemented on 4368 protein data that has been extracted from UniprotKB databank and categories into six different class. The proteins data is high dimensional sequence data and contain a maximum of 48 features.To manipulate the high dimensional sequential protein data with different classification technique, the SPSS has been used as an experimental tool. Different classification techniques give different results for every model and shows that the data are imbalanced for class C4, C5 and C6. The imbalanced data affect the performance of model. In these three classes the precision and recall value is very less or negligible. The experimental results highlight that the C5.0 classification technique accuracy is more suited for protein feature classification and predictions. The C5.0 classification technique gives 95.56% accuracy and also gives high precision and recall value. Finally, we conclude that the features that is selected can be used for function prediction.
Protein Language Models Diverge from Natural Language: Comparative Analysis and Improved Inference
Siebel School of Data Science and Computing（数据科学与计算系）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；DOE Center for Advanced Bioenergy and Bioproducts Innovation（能源与生物产品创新部门）；Chemical and Biomolecular Engineering（化学与生物分子工程系）
Institute of Systems and Physical Biology, Shenzhen Bay Laboratory（深圳湾实验室系统与物理生物学研究所）；School of Electronic and Computer Engineering, Peking University（北京大学电子与计算机工程学院）；Ribopeutic Inc（Ribopeutic公司）
A Linear Classifier Based on Entity Recognition Tools and a Statistical Approach to Method Extraction in the Protein-Protein Interaction Literature
Institute for Biotechnology and Bioengineering（生物技术与生物工程研究所）；University of Minho（米尼奥大学）；School of Informatics and Computing, Indiana University（印第安纳大学信息学与计算学院）；FLAD Computational Biology Collaboratorium（FLAD计算生物学合作中心）；Instituto Gulbenkian de Ciência（古尔本基安科学研究所）；Microsoft Corp.（微软公司）；University of Delaware（特拉华大学）；Center for Bioinformatics and Computational Biology, Delaware Biotechnology Institute, University of Delaware（特拉华大学生物技术研究所生物信息学与计算生物学中心）
Generalizing Jensen and Bregman divergences with comparative convexity and the statistical Bhattacharyya distances with comparable means
École Polytechnique（巴黎综合理工学院）；Sony Computer Science Laboratories Inc（索尼计算机科学研究所）；Data61；Australian National University（澳大利亚国立大学）；University of Sydney（悉尼大学）
Estimation of minimum miscibility pressure (MMP) in impure/pure N2 based enhanced oil recovery process: A comparative study of statistical and machine learning algorithms
University of Shanghai for Science and Technology（上海理工大学）；University of Alberta（阿尔伯塔大学）
DISPROTBENCH: Uncovering the Functional Limits of Protein Structure Prediction Models in Intrinsically Disordered Regions
Institute for Clarity in Documentation（清晰文档研究所）；Inria Paris-Rocquencourt（巴黎-罗克琴堡研究所）；Rajiv Gandhi University（拉贾·甘地大学）；Tsinghua University（清华大学）；Palmer Research Laboratories（帕勒研究实验室）
