Predicting Protein Secondary Structure with Markov Models

Fischer, Paul; Larsen, Simon; Thomsen, Carsten

doi:10.1007/3-540-28084-7_3

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“…Markov Models have been successfully used in a broad range of applications in computational biology including gene finding (e.g. GeneMark [Borodovsky and McIninch (1993)], GenScan [Burge and Karlin (1997)]), protein classification [Yuan (1999), Yakhnenko et al (2005), Fischer et al (2004)] , among others. Salzberg et al (1998) have used Interpolated Markov Models for gene finding.…”

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confidence: 99%

Learning predictive models from massive, semantically disparate data

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Koul

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A kDelegating oracle with D i denoting the oracle EX i f i , X s i , D i. .. 6.2 An example of a mapping with errors and a semantics preserving mapping. 6.3 A schematic representation of reduction between various learning tasks. ix ACKNOWLEDGEMENTS When your graduate studies encompasses stints of two and four years spread over ten years, a marriage, two kids and three jobs it is no wonder that you have a lot of people to thank.

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Section: Summary and Related Workmentioning

confidence: 99%

Learning predictive models from massive, semantically disparate data

Honavar

Koul

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Predicting Protein Secondary Structure with Markov Models

Cited by 1 publication

References 4 publications

Learning predictive models from massive, semantically disparate data

Learning predictive models from massive, semantically disparate data

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