We are analyzing https://link.springer.com/chapter/10.1007/11732990_15.

Title:
CONTRAlign: Discriminative Training for Protein Sequence Alignment | SpringerLink
Description:
In this paper, we present CONTRAlign, an extensible and fully automatic framework for parameter learning and protein pairwise sequence alignment using pair conditional random fields. When learning a substitution matrix and gap penalties from as few as 20 example...
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Keywords {🔍}

google, scholar, article, sequence, alignment, protein, multiple, nucleic, acids, res, information, structure, biol, accuracy, mol, research, contralign, alignments, molecular, biology, paper, privacy, cookies, content, publish, search, computational, conference, learning, random, access, models, proteins, usa, university, data, discriminative, batzoglou, conditional, substitution, sequences, secondary, preview, local, structures, heringa, database, bioinformatics, proc, waterman,

Topics {✒️}

art hand-tuned aligners position-specific scoring matrices hydropathy-based features result environment-specific substitution tables rigorous cross-validated testing position-specific gap penalties homology-extended sequence alignment structure-dependent gap penalties sequence-structure homology recognition multiple sequence alignment article google scholar conference paper research privacy choices/manage cookies computational molecular biology protein sequence alignment multiple alignment toolbox biological sequence analysis multiple sequence alignments protein structure alignment annual international conference secondary structure information protein sequence alignments integrates homology-extended information theoretic perspective conditional random fields random field approach local homology recognition labeling sequence data conditional random field protein structure alignments download preview pdf protein structure analysis european economic area partially local multi acta crystallog sect mart’i-renom predicted local structure fast fourier transform università di padova hidden markov models nucleic acids res 33 nucleic acids res 25 nucleic acids res 27 nucleic acids res 32 nucleic acids res 22 nucleic acids res 31 nucleic acids res 30 conditions privacy policy combining protein sequences protein domain structures

Schema {🗺️}

ScholarlyArticle:
      headline:CONTRAlign: Discriminative Training for Protein Sequence Alignment
      pageEnd:174
      pageStart:160
      image:https://media.springernature.com/w153/springer-static/cover/book/978-3-540-33296-1.jpg
      genre:
         Computer Science
         Computer Science (R0)
      isPartOf:
         name:Research in Computational Molecular Biology
         isbn:
            978-3-540-33296-1
            978-3-540-33295-4
         type:Book
      publisher:
         name:Springer Berlin Heidelberg
         logo:
            url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
            type:ImageObject
         type:Organization
      author:
            name:Chuong B. Do
            affiliation:
                  name:Stanford University
                  address:
                     name:Stanford University, Stanford, USA
                     type:PostalAddress
                  type:Organization
            type:Person
            name:Samuel S. Gross
            affiliation:
                  name:Stanford University
                  address:
                     name:Stanford University, Stanford, USA
                     type:PostalAddress
                  type:Organization
            type:Person
            name:Serafim Batzoglou
            affiliation:
                  name:Stanford University
                  address:
                     name:Stanford University, Stanford, USA
                     type:PostalAddress
                  type:Organization
            type:Person
      keywords:Pairwise Alignment, Solvent Accessibility, Model Topology, Protein Sequence Alignment, Alignment Accuracy
      description:In this paper, we present CONTRAlign, an extensible and fully automatic framework for parameter learning and protein pairwise sequence alignment using pair conditional random fields. When learning a substitution matrix and gap penalties from as few as 20 example alignments, CONTRAlign achieves alignment accuracies competitive with available modern tools. As confirmed by rigorous cross-validated testing, CONTRAlign effectively leverages weak biological signals in sequence alignment: using CONTRAlign, we find that hydropathy-based features result in improvements of 5-6% in aligner accuracy for sequences with less than 20% identity, a signal that state-of-the-art hand-tuned aligners are unable to exploit effectively. Furthermore, when known secondary structure and solvent accessibility are available, such external information is naturally incorporated as additional features within the CONTRAlign framework, yielding additional improvements of up to 15-16% in alignment accuracy for low-identity sequences.
      datePublished:2006
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Book:
      name:Research in Computational Molecular Biology
      isbn:
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         978-3-540-33295-4
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      name:Springer Berlin Heidelberg
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      name:Stanford University
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               name:Stanford University, Stanford, USA
               type:PostalAddress
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      name:Samuel S. Gross
      affiliation:
            name:Stanford University
            address:
               name:Stanford University, Stanford, USA
               type:PostalAddress
            type:Organization
      name:Serafim Batzoglou
      affiliation:
            name:Stanford University
            address:
               name:Stanford University, Stanford, USA
               type:PostalAddress
            type:Organization
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      name:Stanford University, Stanford, USA
      name:Stanford University, Stanford, USA
      name:Stanford University, Stanford, USA
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