Using discriminant function for prediction of subcellular location of prokaryotic proteins.
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Summary
The discriminant function algorithm was introduced to predict the subcellular location of proteins in prokaryotic organisms from their amino-acid composition and is anticipated that, owing to the intimate correlation of the function of a protein with its sub cellular location, it will become a useful tool for the systematic analysis of genome data.
- Type
- article
- Published
- 1998-11-09
- Cited by
- 92
- References
- 10
- OpenAlex
- https://openalex.org/W1979228662
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:23892264
Keywords
Pseudo amino acid composition, Jackknife resampling, Subcellular localization, Consistency (knowledge bases), Function (biology)
References
- Protein Structure Prediction: A Practical Approach
- Relation between amino acid composition and cellular location of proteins.
- Using neural networks for prediction of the subcellular location of proteins.
- Prediction and classification of domain structural classes
- Discrimination of intracellular and extracellular proteins using amino acid composition and residue-pair frequencies.
- Pattern classification and scene analysis
- Prediction of protein structural classes.
- Transmembrane helices predicted at 95% accuracy
- Complete sequence analysis of the genome of the bacterium Mycoplasma pneumoniae.
- A novel approach to predicting protein structural classes in a (20–1)‐D amino acid composition space
Cited by
- PSCL: predicting protein subcellular localization based on optimal functional domains.
- A Computational Algorithm for Functional Clustering of Proteome Dynamics During Development
- Prediction Of Membrane Proteins Using Machine Learning Approaches
- Application of Pseudo Amino Acid Composition for Predicting Protein Subcellular Location: Stochastic Signal Processing Approach
- From Sequence to Sorting : Prediction of Signal Peptides
- Prediction of Membrane Protein Types Based on the Hydrophobic Index of Amino Acids
- Prediction of protein submitochondria locations by hybridizing pseudo-amino acid composition with various physicochemical features of segmented sequence
- Predicting subcellular locations of eukaryotic proteins based on stepwise discriminant analysis
- Predicting protein subcellular location using digital signal processing.
- An Overview on Predicting the Subcellular Location of a Protein
- How does a topological inversion change the evolutionary constraints on membrane proteins?
- Cell-PLoc 2.0: an improved package of web-servers for predicting subcellular localization of proteins in various organisms
- Using neural networks for prediction of subcellular location of prokaryotic and eukaryotic proteins.
- A novel representation of protein sequences for prediction of subcellular location using support vector machines
- Predict Subcellular Locations of Singleplex and Multiplex Proteins by Semi-Supervised Learning and Dimension-Reducing General Mode of Chou's PseAAC
- Naïve Bayes Classifier with Feature Selection to Identify Phage Virion Proteins
- Using subsite coupling to predict signal peptides.
- Prediction of protein structural classes using support vector machines
- Prediction of the subcellular location of prokaryotic proteins based on the hydrophobicity index of amino acids.
- AAIndexLoc: predicting subcellular localization of proteins based on a new representation of sequences using amino acid indices
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