Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation

2011 doctoral thesis by Stefan Mutter at University of Waikato
Place doctoral_thesis Q112381870
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Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation

Summary

Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation is a doctoral thesis[1].

Key Facts

  • Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation authored binary Profile Hidden Markov Models and propositionalisation — author (P50): Stefan Mutter[2].
  • Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation's instance of is recorded as binary Profile Hidden Markov Models and propositionalisation — instance of (P31): doctoral thesis[3].
  • Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation's publisher is recorded as binary Profile Hidden Markov Models and propositionalisation — publisher (P123): Waikato Research Commons[4].
  • Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation's place of publication is recorded as binary Profile Hidden Markov Models and propositionalisation — place of publication (P291): Hamilton[5].
  • Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation's language of work or name is recorded as binary Profile Hidden Markov Models and propositionalisation — language of work or name (P407): English[6].
  • Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation's country of origin is recorded as binary Profile Hidden Markov Models and propositionalisation — country of origin (P495): New Zealand[7].
  • Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation's publication date is recorded as +2011-00-00T00:00:00Z[8].
  • Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation's main subject is recorded as binary Profile Hidden Markov Models and propositionalisation — main subject (P921): bioinformatics[9].
  • Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation's main subject is recorded as binary Profile Hidden Markov Models and propositionalisation — main subject (P921): amino acid[10].
  • Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation's main subject is recorded as binary Profile Hidden Markov Models and propositionalisation — main subject (P921): machine learning[11].
  • Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation's main subject is recorded as binary Profile Hidden Markov Models and propositionalisation — main subject (P921): protein[12].
  • Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation's main subject is recorded as binary Profile Hidden Markov Models and propositionalisation — main subject (P921): generative model[13].
  • Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation's work available at URL is recorded as https://researchcommons.waikato.ac.nz/handle/10289/5299[14].
  • Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation's Handle ID is recorded as 10289/5299[15].
  • Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation's title is recorded as Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation[16].
  • Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation's copyright holder is recorded as binary Profile Hidden Markov Models and propositionalisation — copyright holder (P3931): Stefan Mutter[17].
  • Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation's thesis submitted to is recorded as binary Profile Hidden Markov Models and propositionalisation — thesis submitted to (P4101): University of Waikato[18].
  • Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation's on focus list of Wikimedia project is recorded as binary Profile Hidden Markov Models and propositionalisation — on focus list of Wikimedia project (P5008): NZThesisProject[19].
  • Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation's copyright status is recorded as binary Profile Hidden Markov Models and propositionalisation — copyright status (P6216): copyrighted[20].
  • Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation's online access status is recorded as binary Profile Hidden Markov Models and propositionalisation — online access status (P6954): open access[21].
  • Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation's thesis committee member is recorded as binary Profile Hidden Markov Models and propositionalisation — thesis committee member (P9161): Bernhard Pfahringer[22].
  • Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation's thesis committee member is recorded as binary Profile Hidden Markov Models and propositionalisation — thesis committee member (P9161): Geoffrey Holmes[23].
  • Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation's thesis committee member is recorded as binary Profile Hidden Markov Models and propositionalisation — thesis committee member (P9161): Michael Mayo[24].

Body

Designation and Status

Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation's instance of is recorded as binary Profile Hidden Markov Models and propositionalisation — instance of (P31): doctoral thesis[3].

References

Programmatic citations — every numbered marker resolves to a verifiable graph row below.

Direct Wikidata claims

  1. [3] . wikidata.org.
  2. [2] . wikidata.org.
  3. [4] . wikidata.org.
  4. [5] . wikidata.org.
  5. [6] . wikidata.org.
  6. [7] . wikidata.org.
  7. [8] . wikidata.org.
  8. [9] . hdl.handle.net. hdl.handle.net. Provenance: wikidata.org.
  9. [10] . hdl.handle.net. hdl.handle.net. Provenance: wikidata.org.
  10. [11] . hdl.handle.net. hdl.handle.net. Provenance: wikidata.org.
  11. [12] . hdl.handle.net. hdl.handle.net. Provenance: wikidata.org.
  12. [13] . hdl.handle.net. hdl.handle.net. Provenance: wikidata.org.
  13. [14] . wikidata.org.
  14. [15] . wikidata.org.
  15. [16] . wikidata.org.
  16. [17] . wikidata.org.
  17. [18] . wikidata.org.
  18. [19] . wikidata.org.
  19. [20] . wikidata.org.
  20. [21] . wikidata.org.
  21. [22] . wikidata.org.
  22. [23] . wikidata.org.
  23. [24] . wikidata.org.

Class ancestry

  1. [1] . Wikidata. wikidata.org.

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APA 4ort.xyz Knowledge Graph. (2026). Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation. Retrieved May 3, 2026, from https://4ort.xyz/entity/sequence-based-protein-classification-binary-profile-hidden-markov-models-and-propositionalisation
MLA “Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation.” 4ort.xyz Knowledge Graph, 4ort.xyz, 3 May. 2026, https://4ort.xyz/entity/sequence-based-protein-classification-binary-profile-hidden-markov-models-and-propositionalisation.
BibTeX @misc{4ortxyz_sequence-based-protein-classification-binary-profile-hidden-markov-models-and-propositionalisation_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation}}, year = {2026}, url = {https://4ort.xyz/entity/sequence-based-protein-classification-binary-profile-hidden-markov-models-and-propositionalisation}, note = {Accessed: 2026-05-03}}
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