Daniel G Brown

Ontology type: schema:Person     

Person Info


Daniel G



Publications in SciGraph latest 50 shown

  • 2012-11-26 Towards a practical O(n logn) phylogeny algorithm in ALGORITHMS FOR MOLECULAR BIOLOGY
  • 2012-02-14 PANDAseq: paired-end assembler for illumina sequences in BMC BIOINFORMATICS
  • 2012 Fast Phylogenetic Tree Reconstruction Using Locality-Sensitive Hashing in ALGORITHMS IN BIOINFORMATICS
  • 2011-05-17 More accurate recombination prediction in HIV-1 using a robust decoding algorithm for HMMs in BMC BIOINFORMATICS
  • 2011 Fast Error-Tolerant Quartet Phylogeny Algorithms in COMBINATORIAL PATTERN MATCHING
  • 2011 Towards a Practical O(n logn) Phylogeny Algorithm in ALGORITHMS IN BIOINFORMATICS
  • 2010-01-18 Decoding HMMs using the k best paths: algorithms and applications in BMC BIOINFORMATICS
  • 2010-01-18 New decoding algorithms for Hidden Markov Models using distance measures on labellings in BMC BIOINFORMATICS
  • 2006 New Bounds for Motif Finding in Strong Instances in COMBINATORIAL PATTERN MATCHING
  • 2005-11-17 Ancestral sequence alignment under optimal conditions in BMC BIOINFORMATICS
  • 2005 Sharper Upper and Lower Bounds for an Approximation Scheme for Consensus-Pattern in COMBINATORIAL PATTERN MATCHING
  • 2004 New Algorithms for Multiple DNA Sequence Alignment in ALGORITHMS IN BIOINFORMATICS
  • 2004 Multiple Vector Seeds for Protein Alignment in ALGORITHMS IN BIOINFORMATICS
  • 2004 A New Integer Programming Formulation for the Pure Parsimony Problem in Haplotype Analysis in ALGORITHMS IN BIOINFORMATICS
  • 2004 Optimizing Multiple Spaced Seeds for Homology Search in COMBINATORIAL PATTERN MATCHING
  • 2004 The Most Probable Labeling Problem in HMMs and Its Application to Bioinformatics in ALGORITHMS IN BIOINFORMATICS
  • 2003-05-27 Optimal Spaced Seeds for Hidden Markov Models, with Application to Homologous Coding Regions in COMBINATORIAL PATTERN MATCHING
  • 2003 Vector Seeds: An Extension to Spaced Seeds Allows Substantial Improvements in Sensitivity and Specificity in ALGORITHMS IN BIOINFORMATICS
  • 2003 Optimal DNA Signal Recognition Models with a Fixed Amount of Intrasignal Dependency in ALGORITHMS IN BIOINFORMATICS
  • JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "affiliation": [
            "affiliation": {
              "id": "http://www.grid.ac/institutes/grid.46078.3d", 
              "type": "Organization"
            "isCurrent": true, 
            "type": "OrganizationRole"
            "id": "http://www.grid.ac/institutes/grid.7634.6", 
            "type": "Organization"
        "familyName": "Brown", 
        "givenName": "Daniel G", 
        "id": "sg:person.0642727740.54", 
        "sameAs": [
        "sdDataset": "persons", 
        "sdDatePublished": "2022-09-02T16:34", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        "sdSource": "s3://com-springernature-scigraph/baseset/20220902/entities/gbq_results/person/person_716.jsonl", 
        "type": "Person"

    Download the RDF metadata as:  json-ld nt turtle xml License info


    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/person.0642727740.54'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/person.0642727740.54'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/person.0642727740.54'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/person.0642727740.54'


    Preview window. Press ESC to close (or click here)