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Using Functional Domain Composition and Support Vector Machines for Prediction of Protein Subcellular Location
Open accessAbstract
Proteins are generally classified into the following 12 subcellular locations: 1) chloroplast, 2) cytoplasm, 3) cytoskeleton, 4) endoplasmic reticulum, 5) extracellular, 6) Golgi apparatus, 7) lysosome, 8) mitochondria, 9) nucleus, 10) peroxisome, 11) plasma membrane, and 12) vacuole. Because the function of a protein is closely correlated with its subcellular location, with the rapid increase in new protein sequences entering into databanks, it is vitally important for both basic research and pharmaceutical industry to establish a high throughput tool for predicting protein subcellular location. In this paper, a new concept, the so-called "functional domain composition" is introduced. Based on the novel concept, the representation for a protein can be defined as a vector in a high-dimensional space, where each of the clustered functional domains derived from the protein universe serves as a vector base. With such a novel representation for a protein, the support vector machine (SVM) algorithm is introduced for predicting protein subcellular location. High success rates are obtained by the self-consistency test, jackknife test, and independent dataset test, respectively. The current approach not only can play an important complementary role to the powerful covariant discriminant algorithm based on the pseudo amino acid composition representation (Chou, K. C. (2001) Proteins Struct. Funct. Genet. 43, 246-255; Correction (2001) Proteins Struct. Funct. Genet. 44, 60), but also may greatly stimulate the development of this area.
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“The principles of SVM were introduced in Chou and Cai [60,61], and more details are provided in Cristianini [62].”
From An Efficient Classifier for Alzheimer’s Disease Genes Identification (2018) · cites this paper as [60]
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Metadata
- Authors
- Chou, K.-C., & Cai, Y.-D.
- Affiliations
- Chinese Academy of Sciences (CN)
- Corresponding author
- Cai, Yu-Dong
- ORCID
- Cai, Yu-Dong
- Venue
- Journal of Biological Chemistry
- Publisher
- Elsevier BV
- Published
- 2002-11
- Volume
- 277
- Issue
- 48
- Pages
- 45765-45769
- ISSN
- 0021-9258 (ISSN-L)
- OpenAlex
- W1992577925
- Cited by
- 483
- References
- 41
- Citations by year
- 2025: 2 · 2024: 2 · 2023: 3 · 2022: 9 · 2021: 7 · 2020: 11 · 2019: 18 · 2018: 27 · 2017: 16 · 2016: 20 · 2015: 17 · 2014: 22 · 2013: 23 · 2012: 32
- FWCI
- 7.65
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Biological Chemistry (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Fractal and DNA sequence analysis
- Keywords
- support vector machine, protein function prediction, protein sequence representation, pseudo amino acid composition, jackknife test, high-dimensional feature vectors
- MeSH terms
- Proteins, Subcellular Fractions
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