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 was published by 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 was released on 2011^[8].
• Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation's main subject is 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 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 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 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 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 title is recorded as Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation^[15].
• 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^[16].
• 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^[17].
• 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^[18].
• 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^[19].
• 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^[20].
• 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^[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): Geoffrey Holmes^[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): Michael Mayo^[23].
• Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation's thesis submitted for degree is recorded as binary Profile Hidden Markov Models and propositionalisation — thesis submitted for degree (P13338): Doctor of Philosophy^[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].