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Algorithms for Molecular Biology [electronic resource] / edited by Peter Stadler, Burkhard Morgenstern.

Contributor(s): Material type: Continuing resourceContinuing resourcePublisher: London : BioMed Central : Imprint: BioMed Central. Description: online resourceISSN:
  • 1748-7188
Subject(s): Online resources: Summary: Algorithms for Molecular Biology publishes articles on novel algorithms for biological sequence and structure analysis, phylogeny reconstruction, and combinatorial algorithms and machine learning. Areas of interest include but are not limited to: algorithms for RNA and protein structure analysis, gene prediction and genome analysis, comparative sequence analysis and alignment, phylogeny, gene expression, machine learning, and combinatorial algorithms. Where appropriate, manuscripts should describe applications to real-world data. However, pure algorithm papers are also welcome if future applications to biological data are to be expected, or if they address complexity or approximation issues of novel computational problems in molecular biology. Articles about novel software tools will be considered for publication if they contain some algorithmically interesting aspects.
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Algorithms for Molecular Biology publishes articles on novel algorithms for biological sequence and structure analysis, phylogeny reconstruction, and combinatorial algorithms and machine learning. Areas of interest include but are not limited to: algorithms for RNA and protein structure analysis, gene prediction and genome analysis, comparative sequence analysis and alignment, phylogeny, gene expression, machine learning, and combinatorial algorithms. Where appropriate, manuscripts should describe applications to real-world data. However, pure algorithm papers are also welcome if future applications to biological data are to be expected, or if they address complexity or approximation issues of novel computational problems in molecular biology. Articles about novel software tools will be considered for publication if they contain some algorithmically interesting aspects.

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