ArticleTheScientificWorldJournal2014
An empirical study of different approaches for protein classification.
Article in TheScientificWorldJournal, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- PhageLeads: Rapid Assessment of Phage Therapeutic Suitability Using an Ensemble Machine Learning Approach.Viruses · 2022Article
- Prediction of Plant Resistance Proteins Based on Pairwise Energy Content and Stacking Framework.Frontiers in plant science · 2022Article
- PSSMCOOL: a comprehensive R package for generating evolutionary-based descriptors of protein sequences from PSSM profiles.Biology methods & protocols · 2022Article
- CpACpP:ACS omega · 2021Article
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- Use Chou's 5-Step Rule to Predict DNA-Binding Proteins with Evolutionary Information.BioMed research international · 2020Article
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- PTPD: predicting therapeutic peptides by deep learning and word2vec.BMC bioinformatics · 2019Article
- Prediction of Self-Interacting Proteins from Protein Sequence Information Based on Random Projection Model and Fast Fourier Transform.International journal of molecular sciences · 2019Article
- Sequence-based Prediction of Protein-Protein Interactions Using Gray Wolf Optimizer-Based Relevance Vector Machine.Evolutionary bioinformatics online · 2019Article
- Article
- Prediction of Protein-Protein Interactions from Amino Acid Sequences Based on Continuous and Discrete Wavelet Transform Features.Molecules (Basel, Switzerland) · 2018Article
- Improved detection of DNA-binding proteins via compression technology on PSSM information.PloS one · 2017Article
- Identification of Protein-Protein Interactions via a Novel Matrix-Based Sequence Representation Model with Amino Acid Contact Information.International journal of molecular sciences · 2016Article
- Toward a General-Purpose Heterogeneous Ensemble for Pattern Classification.Computational intelligence and neuroscience · 2015Article
- Multi-level machine learning prediction of protein-protein interactions in Saccharomyces cerevisiae.PeerJ · 2015Article
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Authors and funding
3 authors.
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Abstract
Many domains would benefit from reliable and efficient systems for automatic protein classification. An area of particular interest in recent studies on automatic protein classification is the exploration of new methods for extracting features from a protein that work well for specific problems. These methods, however, are not generalizable and have proven useful in only a few domains. Our goal is to evaluate several feature extraction approaches for representing proteins by testing them across multiple datasets. Different types of protein representations are evaluated: those starting from the position specific scoring matrix of the proteins (PSSM), those derived from the amino-acid sequence, two matrix representations, and features taken from the 3D tertiary structure of the protein. We also test new variants of proteins descriptors. We develop our system experimentally by comparing and combining different descriptors taken from the protein representations. Each descriptor is used to train a separate support vector machine (SVM), and the results are combined by sum rule. Some stand-alone descriptors work well on some datasets but not on others. Through fusion, the different descriptors provide a performance that works well across all tested datasets, in some cases performing better than the state-of-the-art.
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