Hidden Markov models for sequence analysis: extension and analysis of the basic method

Hughey, Richard; Krogh, Anders

doi:10.1093/bioinformatics/12.2.95

Cited by 309 publications

(247 citation statements)

References 15 publications

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“…Today, the most powerful sequence-based comparison methods use sets of aligned sequences, either as profiles [29], hidden Markov models (HMMs) [30][31][32] or position-specific scoring matrices (PSSMs) [4,33]. There are also several profile databases, including PFAM [34], SUPERFAMILY [35] and IMPALA [36], which can be searched using these methods.…”

Section: Progress In Sequence Similarity Searchingmentioning

confidence: 99%

The limits of protein sequence comparison?

Pearson

Sierk

2005

Current Opinion in Structural Biology

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Modern sequence alignment algorithms are used routinely to identify homologous proteins, proteins that share a common ancestor. Homologous proteins always share similar structures and often have similar functions. Over the past 20 years, sequence comparison has become both more sensitive, largely because of profile-based methods, and more reliable, because of more accurate statistical estimates. As sequence and structure databases become larger, and comparison methods become more powerful, reliable statistical estimates will become even more important for distinguishing similarities that are due to homology from those that are due to analogy (convergence). The newest sequence alignment methods are more sensitive than older methods, but more accurate statistical estimates are needed for their full power to be realized.

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Section: Progress In Sequence Similarity Searchingmentioning

confidence: 99%

The limits of protein sequence comparison?

Pearson

Sierk

2005

Current Opinion in Structural Biology

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“…HMMs have been widely and successfully applied to a large number of problems, such as speech recognition [4], DNA and protein modeling [5], and gesture recognition [6]. We show in this paper that HMMs are also effective in classification of MMOG players, and, in particular, have higher recognition performance than AMBR based on action frequencies.…”

Section: Introductionmentioning

confidence: 74%

MMOG Player Classification Using Hidden Markov Models

Matsumoto

Thawonmas

2004

Entertainment Computing – ICEC 2004

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Abstract. In this paper, we describe our work on classification of players in Massively Multiplayer Online Games using Hidden Markov Models based on player action sequences. In our previous work, we have discussed a classification approach using a variant of Memory Based Reasoning based on player action frequencies. That approach, however, does not exploit time structures hidden in action sequences of the players. The experimental results given in this paper show that Hidden Markov Models have higher recognition performance than our previous approach, especially for classification of players of different types but having similar action frequencies.

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“…But the HMMs do not find an specific sequence, they find a family of subsequences, i.e. [15,16]. If we were to use a method such as HMMs then we can substitute any subsequence described by the family for the same tag.…”

Section: The Use Of N-grams As a Reduction Methodsmentioning

confidence: 99%

On the Role of Information Compaction to Intrusion Detection

Godínez

Hutter²,

Monroy

2005

Advanced Distributed Systems

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Abstract. An intrusion detection system (IDS) usually has to analyse Giga-bytes of audit information. In the case of anomaly IDS, the information is used to build a user profile characterising normal behaviour. Whereas for misuse IDSs, it is used to test against known attacks. Probabilistic methods, e.g. hidden Markov models, have proved to be suitable to profile formation but are prohibitively expensive. To bring these methods into practise, this paper aims to reduce the audit information by folding up subsequences that commonly occur within it. Using n-grams language models, we have been able to successfully identify the n-grams that appear most frequently. The main contribution of this paper is a n-gram extraction and identification process that significantly reduces an input log file keeping key information for intrusion detection. We reduced log files by a factor of 3.6 in the worst case and 4.8 in the best case. We also tested reduced data using hidden Markov models (HMMs) for intrusion detection. The time needed to train the HMMs is greatly reduced by using our reduced log files, but most importantly, the impact on both the detection and false positive ratios are negligible.

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Hidden Markov models for sequence analysis: extension and analysis of the basic method

Cited by 309 publications

References 15 publications

The limits of protein sequence comparison?

The limits of protein sequence comparison?

MMOG Player Classification Using Hidden Markov Models

On the Role of Information Compaction to Intrusion Detection

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