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5th IEEE Symposium on Bioinformatics and Bioengineering

作者： Li, YH Lauria, M Bundschuh, R Ohio State Univ Dept Comp Sci & Engn Columbus OH 43210 USA

ISBN: (纸本)0769524761

PSI-BLAST remains one of the popular tools for searching remote homologs in sequence databases. We recently demonstrated that hybrid alignment can function as the alignment core for PSI-BLAST without loss of sensitivity. Here, we start to exploit the benefits of hybrid alignment. We show that incorporating information about the suboptimal alignments, otherwise ignored in PSI-BLAST already improves the sensitivity of our enhanced version of PSI-BLAST More interestingly, we find a set of sequences on which our tool disagrees with the classification given by SCOP Careful examination points to a possible misclassification in SCOP Cross-referencing with two other methods of protein structure classification, CATH and DALI, supports this view, indicating that the enriched information from suboptimal alignments is valuable for detecting more weakly homologous sequences.

关键词： sequence alignment hybrid algorithm PSI-BLAST suboptimal alignment forward-backward algorithm

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Optimization of HMM by the tabu search algorithm

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JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 2004年第5期20卷 949-957页

作者： Chen, TY Mei, XD Pan, JS Sun, SH Natl Kaohsiung Univ Appl Sci Dept Elect Engn Kaohsiung 807 Taiwan Harbin Inst Technol Dept Automat Test Measurement & Control Harbin 150001 Peoples R China

In this paper, a simple version of the tabu search algorithm is employed to train a Hidden Markov Model (HMM) to search out the optimal parameter structure of HMM for automatic speech recognition. The proposed TS-HMM training provides a mechanism that allows the search process to escape from a local optimum and obtain a near global optimum. Experimental results show that the TS-HMM training has a higher probability of finding the optimal model parameters than traditional algorithms do.

关键词： tabu search hidden Markov model forward-backward algorithm speech recognition global optimum

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Probabilistic methods of identifying genes in prokaryotic genomes: Connections to the FIMM theory

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BRIEFINGS IN BIOINFORMATICS 2004年第2期5卷 118-130页

作者： Azad, RK Borodovsky, M Georgia Inst Technol Sch Biol Atlanta GA 30332 USA Georgia Inst Technol Sch Biomed Engn Atlanta GA 30332 USA Georgia Inst Technol Ctr Bioinformat & Computat Biol Atlanta GA 30332 USA

In this paper, we review developments in probabilistic methods of gene recognition in prokaryotic genomes with the emphasis on connections to the general theory of hidden Markov models (HMM). We show that the Bayesian method implemented in GeneMark, a frequently used gene-finding tool, can be augmented and reintroduced as a rigorous forward-backward (FB) algorithm for local posterior decoding described in the HMM theory. Another earlier developed method, prokaryotic ***, uses a modification of the Viterbi algorithm for HMM with duration to identify the most likely global path through hidden functional states given the DNA sequence. GeneMark and *** programs are worth using in concert for analysing prokaryotic DNA sequences that arguably do not follow any exact mathematical model. The new extension of GeneMark using the FB algorithm was implemented in the software program ***. Given the DNA sequence, this program determines an a posteriori probability for each nucleotide to belong to coding or non-coding region. Also, for any open reading frame (ORF), it assigns a score defined as a probabilistic measure of all paths through hidden states that traverse the ORF as a coding region. The prediction accuracy of *** determined in our tests was compared favourably to the accuracy of the initial (standard) GeneMark program. Comparison to the prokaryotic *** has also demonstrated a certain, yet species-specific, degree of improvement in raw gene detection, ie detection of correct reading frame (and stop codon). The accuracy of exact gene prediction, which is concerned about precise prediction of gene start (which in a prokaryotic genome unambiguously defines the reading frame and stop codon, thus, the whole protein product), still remains more accurate in GeneMarks, which uses more elaborate HMM to specifically address this task.

关键词： prokoryotic gene prediction Markov model hidden Markov model forward-backward algorithm

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Filtering recursions for calculating likelihoods for queues based on inter-departure time data

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STATISTICS AND COMPUTING 2004年第3期14卷 261-266页

作者： Fearnhead, P Univ Lancaster Dept Math & Stat Lancaster LA1 4YF England

We consider inference for queues based on inter-departure time data. Calculating the likelihood for such models is difficult, as the likelihood involves summing up over the (exponentially-large) space of realisations of the arrival process. We demonstrate how a likelihood recursion can be used to calculate this likelihood efficiently for the specific cases of M/G/1 and Er/G/1 queues. We compare the sampling properties of the mles to the sampling properties of estimators, based on indirect inference, which have previously been suggested for this problem.

关键词： Er/G/1 queues forward-backward algorithm indirect inference likelihood recursions M/G/1 queues missing data

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Exact filtering for partially observed continuous time models

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JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY 2004年第3期66卷 771-789页

作者： Fearnhead, P Meligkotsidou, L Univ Lancaster Dept Math & Stat Lancaster LA1 4YF England

The forward-backward algorithm is an exact filtering algorithm which can efficiently calculate likelihoods, and which can be used to simulate from posterior distributions. Using a simple result which relates gamma random variables with different rates, wig show how the forward-backward algorithm can be used to calculate the distribution of a sum of gamma random variables, and to simulate from their joint distribution given their sum. One application is to calculating the density of the time of a specific event in a Markov process, as this time is the sum of exponentially distributed interevent times. This enables us to apply the forward-backward algorithm to a range of new problems. We demonstrate our method on three problems: calculating likelihoods and simulating allele frequencies under a non-neutral population genetic model, analysing a stochastic epidemic model and simulating speciation times in phylogenetics.

关键词： Data augmentation EM algorithm Epidemics forward-backward algorithm Gibbssampling Markov chain Monte Carlo methods Phylogenetics Population genetics Susceptible-infected-removed epidemic Wright''s formula

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A hidden semi-Markov model with missing data and multiple observation sequences for mobility tracking

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SIGNAL PROCESSING 2003年第2期83卷 235-250页

作者： Yu, SZ Kobayashi, H Princeton Univ Dept Elect Engn Princeton NJ 08544 USA

A hidden Markov model (HMM) encompasses a large class of stochastic process models and has been successfully applied to a number of scientific and engineering problems, including speech and other pattern recognition problems, and DNA sequence comparison. A hidden semi-Markov model (HSMM) is an extension of HMM, designed to remove the constant or geometric distributions of the state durations assumed in HMM. A larger class of practical problems can be appropriately modeled in the setting of HSMM. A major restriction is found, however, in both conventional HMM and HSMM, i.e., it is generally assumed that there exists at least one observation associated with every state that the hidden Markov chain takes on. We will remove this assumption and consider the following situations: (i) observation data may be missing for some intervals;and (ii) there are multiple observation streams that are not necessarily synchronous to each other and may have different "emission distributions" for the same state. We propose a new and computationally efficient forward-backward algorithm for HSMM with missing observations and multiple observation sequences. The required computational amount for the forward and backward variables is reduced to O(D), where D is the maximum allowed duration in a state. Finally, we will apply the extended HSMM to estimate the mobility model parameters for the Internet service provisioning in wireless networks. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： hidden Markov model (HMM) HMM with explicit duration hidden semi-Markov Model (HSMM) missing data multiple observations Ferguson algorithm forward-backward algorithm expectation-maximization (EM) algorithm maximum likelihood (ML) estimation maximum a posteriori probability (MAP) estimation mobility modeling wireless Internet service

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Path partitions and forward-only trellis algorithms

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IEEE TRANSACTIONS ON INFORMATION THEORY 2003年第1期49卷 38-52页

作者： Ma, X Kavcic, A Harvard Univ Div Engn & Appl Sci Cambridge MA 02138 USA

This is a semitutorial paper on trellis-based algorithms. We argue that most decoding/detection algorithms described on trellises can be formulated as path-partitioning algorithms, with proper definitions of mappings from subsets of paths to metrics of subsets. Thereby, the only two operations needed are path-concatenation and path-collection, which play the roles of multiplication and addition, respectively. Furthermore, we show that the trellis structure permits the path-partitioning algorithms to be formulated as forward-only algorithms (with structures resembling the Viterbi algorithm), thus eliminating the need for backward computations regardless of what task needs to be performed on the trellis. While all of the actual decoding/detection algorithms presented here are rederivations of variations of previously known methods, we believe that the exposition of the algorithms in a unified manner as forward-only path-partitioning algorithms is the most intuitive manner in which to generalize the Viterbi algorithm. We also believe that this approach may, in fact, influence the practical implementation of the algorithms as well as influence the construction of other forward-only algorithms (e.g., byte-wise forward-only detection algorithms).

关键词： Babl-Cocke-Jelinek-Raviv (BCJR) algorithm forward-backward algorithm forward-only algorithm Markov processes sum-product algorithm trellis graph Viterbi algorithm

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VLSI architectures for the MAP algorithm

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IEEE TRANSACTIONS ON COMMUNICATIONS 2003年第2期51卷 175-185页

作者： Boutillon, E Gross, WJ Gulak, PG Univ Bretagne Sud LESTER F-56325 Lorient France Univ Toronto Dept Elect & Comp Engn Toronto ON M5S 3G4 Canada

This paper presents several techniques for the very large-scale integration (VLSI) implementation of the maximum a posteriori (MAP) algorithm. In general, knowledge about the implementation of the Viterbi algorithm can be applied to the MAP algorithm. Bounds are derived for the dynamic range of the state metrics which enable the designer to optimize the word length. The computational kernel of the algorithm is the Add-MAX* operation, which is the Add-Compare-Select operation of the Viterbi algorithm with an added offset. We show that the critical path of the algorithm can be reduced if the Add-MAX* operation is reordered into an Offset-Add-Compare-Select operation by adjusting the location of registers. A general scheduling for the MAP algorithm is presented which gives the tradeoffs between computational complexity, latency, and memory size. Some of these architectures eliminate the need for RAM blocks with unusual form factors or can replace the RAM with registers. These architectures are suited to VLSI implementation of turbo decoders.

关键词： forward-backward algorithm MAP estimation turbo codes very large-scale integration (VLSI) Viterbi decoding

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HMM-based channel error mitigation and its application to distributed speech recognition

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SPEECH COMMUNICATION 2003年第4期41卷 549-561页

作者： Peinado, AM Sánchez, V Pérez-Córdoba, JL de la Torre, A Univ Granada Fac Ciencias Dept Elect & Tecnol Comp Granada 18071 Spain

The emergence of distributed speech recognition has generated the need to mitigate the degradations that the transmission channel introduces in the speech features used for recognition. This work proposes a hidden Markov model (HMM) framework from which different mitigation techniques oriented to wireless channels can be derived. First, we study the performance of two techniques based on the use of a minimum mean square error (MMSE) estimation, a raw MMSE and a forward MMSE estimation, over additive white Gaussian noise (AWGN) channels. These techniques are also adapted to bursty channels. Then, we propose two new mitigation methods specially suitable for bursty channels. The first one is based on a forward-backward MMSE estimation and the second one on the well-known Viterbi algorithm. Different experiments are carried out, dealing with several issues such as the application of hard decisions on the received bits or the influence of the estimated channel SNR. The experimental results show that the HMM-based techniques can effectively mitigate channel errors, even in very poor channel conditions. (C) 2003 Elsevier B.V. All rights reserved.

关键词： channel error mitigation distributed speech recognition hidden Markov models wireless communications forward-backward algorithm Viterbi algorithm

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Estimating hidden semi-Markov chains from discrete sequences

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JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS 2003年第3期12卷 604-639页

作者： Guédon, Y Univ Montpellier 2 INRA CNRS CIRADUnite Mixte Rech F-34398 Montpellier 5 France

This article addresses the estimation of hidden semi-Markov chains from nonstationary discrete sequences. Hidden semi-Markov chains are particularly useful to model the succession of homogeneous zones or segments along sequences. A discrete hidden semi-Markov chain is composed of a nonobservable state process, which is a semi-Markov chain, and a discrete output process. Hidden semi-Markov chains generalize hidden Markov chains and enable the modeling of various durational structures. From an algorithmic point of view, a new forward-backward algorithm is proposed whose complexity is similar to that of the Viterbi algorithm in terms of sequence length (quadratic in the worst case in time and linear in space). This opens the way to the maximum likelihood estimation of hidden semi-Markov chains from long sequences. This statistical modeling approach is illustrated by the analysis of branching and flowering patterns in plants.

关键词： censoring EM algorithm forward-backward algorithm nonparametric maximum likelihood plant structure analysis smoothing algorithm Viterbi algorithm

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