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Particle Swarm Optimization | Peer Reviewed Journals

Open Access

Particle Swarm Optimization

In computational science, particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. It solves a problem by having a population of candidate solutions, here dubbed particles, and moving these particle.

Conference Proceedings

Automatic reconstruction and modeling of biocatalyst systems for the production of specific biochemical compounds
Frederic CADET and Nicolas Fontaine
Posters & Accepted Abstracts: Journal of Proteomics & Bioinformatics
ADPredict: ADP-ribosylation sites prediction based on physicochemical and structure descriptors
Matteo Lo Monte, Candida Manelfi and Andrea R Beccari
Scientific Tracks Abstracts: Journal of Proteomics & Bioinformatics
Improved SERS nanodumbbells design for femtomolar-sensitive screening disease-associated biomarkers
Nekane Guarrotxena
Scientific Tracks Abstracts: Journal of Proteomics & Bioinformatics
Insights into the combinatorial biosynthesis and tailoring of fungal polyketides
Yuquan Xu, Liwen Zhang, Min Lin and Istvan Molnar
Posters & Accepted Abstracts: Current Synthetic and Systems Biology
Use of synthetic proteins and future trends: The example of calstabin
Jean A Boutin
Scientific Tracks Abstracts: Current Synthetic and Systems Biology
Metatranscriptomics of colonic lesions in inflammatory bowel disease
Marcus J Claesson1, Feargal Ryan1, Emilo Laserna1, John O callaghan1, Ian B Jeffery1, Aldert Zomer2, Aine Fanning1 and Fergus Shanahan1
Accepted Abstracts: Transcriptomics: Open Access

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