Amino Acid Substitution Matrices from an Artificial Neural Network Model
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Summary
An artificial neural network model is used to predict probabilities of amino acid substitutions with alignment samples of different evolutionary distances to generate substitution matrices suitable for detecting relationships at any chosen evolutionary distance.
- Type
- article
- Published
- 2001-10-01
- Cited by
- 15
- References
- 26
- OpenAlex
- https://openalex.org/W2013221409
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:13196530
Keywords
Substitution (logic), Artificial neural network, Amino acid substitution, Distance matrix, Matrix (chemical analysis)
References
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- Protein structure alignment.
- Improved tools for biological sequence comparison.
- The rapid generation of mutation data matrices from protein sequences
- Amino acid encoding schemes from protein structure alignments: multi-dimensional vectors to describe residue types.
- Recognition of analogous and homologous protein folds: analysis of sequence and structure conservation.
- Basic local alignment search tool.
- A general method applicable to the search for similarities in the amino acid sequence of two proteins.
- Identification of common molecular subsequences.
- Protein structure comparison using iterated double dynamic programming
- Database of homology‐derived protein structures and the structural meaning of sequence alignment
- Assessing the accuracy of prediction algorithms for classification: an overview
- CATH--a hierarchic classification of protein domain structures.
- Performance evaluation of amino acid substitution matrices
- Amino acid substitution matrices from protein blocks.
Cited by
- A Collection of Amino Acid Replacement Matrices Derived from Clusters of Orthologs
- Protein fold recognition using neural networks.
- Advanced stochastic protein sequence analysis (Neue Ansätze zur Analyse von Proteinsequenzen mit Hidden-Markov-Modellen)
- Ideal amino acid exchange forms for approximating substitution matrices
- Statistical Evaluation of Local Alignment Features Predicting Allergenicity Using Supervised Classification Algorithms
- Amino acid encoding schemes from protein structure alignments: multi-dimensional vectors to describe residue types.
- Detailed assessment of homology detection using different substitution matrices
- A revised description of Gyrodactylus cichlidarum Paperna, 1968 (Gyrodactylidae) from the Nile tilapia, Oreochromis niloticus niloticus (Cichlidae), and its synonymy with G. niloticus Cone, Arthur et Bondad-Reantaso, 1995.
- Threading Using Neural nEtwork (TUNE): the measure of protein sequence-structure compatibility
- Mathematical models for studying the properties of the genetic code
- Loss-of-function, gain-of-function and dominant-negative mutations have profoundly different effects on protein structure
- Loss-of-function, gain-of-function and dominant-negative mutations have profoundly different effects on protein structure: implications for variant effect prediction
- A Methodology for Determining Amino-Acid Substitution Matrices from Set Covers
- Distributional Proteomics: Modelling Amino Acid Relationships by Measuring Their Patterns of Statistical Occurrence Across Proteins.
- Statistical evaluation of local alignment features for prediction of protein allergenicity using supervised classification algorithms
- Organism specific Amino Acid Substitution Matrices
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