ArticleBioMed research international2014
Protein sequence classification with improved extreme learning machine algorithms.
Article in BioMed research international, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- InteracTor: Feature engineering and explainable AI for profiling protein structure-interaction-function relationships.PLoS computational biology · 2025Article
- ProtInteract: A deep learning framework for predicting protein-protein interactions.Computational and structural biotechnology journal · 2023Article
- Systematic analysis of HD-ZIP transcription factors in sesame genome and gene expression profiling of SiHD-ZIP class I entailing drought stress responses at early seedling stage.Molecular biology reports · 2022Article
- Computational Method for Classification of Avian Influenza A Virus Using DNA Sequence Information and Physicochemical Properties.Frontiers in genetics · 2021Article
- A two-stage approach towards protein secondary structure classification.Medical & biological engineering & computing · 2020Article
- Article
- CirRNAPL: A web server for the identification of circRNA based on extreme learning machine.Computational and structural biotechnology journal · 2020Article
- iPseU-CNN: Identifying RNA Pseudouridine Sites Using Convolutional Neural Networks.Molecular therapy. Nucleic acids · 2019Article
- A Novel Computational Method for Detecting DNA Methylation Sites with DNA Sequence Information and Physicochemical Properties.International journal of molecular sciences · 2018Article
- Discriminating cirRNAs from other lncRNAs using a hierarchical extreme learning machine (H-ELM) algorithm with feature selection.Molecular genetics and genomics : MGG · 2018Article
- A Novel Modeling in Mathematical Biology for Classification of Signal Peptides.Scientific reports · 2018Article
- Virtual screening by a new Clustering-based Weighted Similarity Extreme Learning Machine approach.PloS one · 2018Article
- Predicting protein-protein interactions via multivariate mutual information of protein sequences.BMC bioinformatics · 2016Article
- Label noise in subtype discrimination of class C G protein-coupled receptors: A systematic approach to the analysis of classification errors.BMC bioinformatics · 2015Article
- Transformer-based classification and interpretability of NR3C1 expression patterns in OSCC: Metabolic adaptation insights.Journal of oral biology and craniofacial researchArticle
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Authors and funding
2 authors.
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Abstract
Precisely classifying a protein sequence from a large biological protein sequences database plays an important role for developing competitive pharmacological products. Comparing the unseen sequence with all the identified protein sequences and returning the category index with the highest similarity scored protein, conventional methods are usually time-consuming. Therefore, it is urgent and necessary to build an efficient protein sequence classification system. In this paper, we study the performance of protein sequence classification using SLFNs. The recent efficient extreme learning machine (ELM) and its invariants are utilized as the training algorithms. The optimal pruned ELM is first employed for protein sequence classification in this paper. To further enhance the performance, the ensemble based SLFNs structure is constructed where multiple SLFNs with the same number of hidden nodes and the same activation function are used as ensembles. For each ensemble, the same training algorithm is adopted. The final category index is derived using the majority voting method. Two approaches, namely, the basic ELM and the OP-ELM, are adopted for the ensemble based SLFNs. The performance is analyzed and compared with several existing methods using datasets obtained from the Protein Information Resource center. The experimental results show the priority of the proposed algorithms.
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