Genetic algorithm
2 methods · 2004–2020
Genetic algorithm: Candidate sequences evolved by mutation, crossover and selection against a spectrum-matching score, searching the space of peptides without building a spectrum graph.
Candidate sequences evolved by mutation, crossover and selection against a spectrum-matching score, searching the space of peptides without building a spectrum graph.
The earliest of its 2 methods is Genetic-algorithm sequence optimization (PNNL) (2004).
| Methods | 2 |
| Papers describing them | 5 |
| Authors | 10 |
| Active | 2004-06-10 to 2020-01-01 |
| Kinds | algorithm (2) |
| Acquisition | DDA (2) |
Methods (2)
Oldest first, by the paper that describes each one.
- Genetic-algorithm sequence optimization (PNNL) (2004): Reconstructs peptide sequences from tandem spectra with a genetic algorithm, later extended to parallel multi-objective optimisation with Pareto ranking of conflicting scores.
- GA-Novo (2019): Genetic-algorithm approach for de novo peptide sequencing from tandem mass spectra.
Papers describing them (5)
- Constrained De Novo peptide identification via multi-objective optimization (2004, 18th International Parallel and Distributed Processing Symposium, 2004. Proceedings, peer-reviewed)
- Sequence optimization as an alternative to de novo analysis of tandem mass spectrometry data (2004, Bioinformatics, peer-reviewed)
- GA-Novo: De Novo Peptide Sequencing via Tandem Mass Spectrometry using Genetic Algorithm (2019, arXiv, preprint)
- GA-Novo: De Novo Peptide Sequencing via Tandem Mass Spectrometry Using Genetic Algorithm (2019, Lecture Notes in Computer Science, peer-reviewed)
- Evolutionary Algorithms for Improving De Novo Peptide Sequencing (2020, thesis)