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