Hidden Markov methods

Farrell, J. A., S. Pang, and W. Li. Plume mapping via hidden Markov methods. Ieee Trans. Syst., Man, and Cybernetics—Part B Cybernetics 33(6), 850-863 (2003). 

Building sequence profiles or Hidden Markov Models to perform more sensitive homology searches. A sequence profile contains information about the variability of every sequence position, improving structure prediction methods (secondary structure prediction). Sequence profile searches have become readily available through the introduction of PsiBLAST [4]... 

The parsing of the transporter sequences into the TM domains shown in Fig. 1A represents the consensus result of three different methods. Average hydrophobicity was calculated with ProperTM using different window sizes and the Kyte and Doolittle scale (7). TMHMM, a hidden Markov model-based approach (8), and PHDHTM, a profile-based neural network method (9), were then utilized to refine the predictions. 

Recently, a hidden Markov model-based method was developed to identify LOH from tumor samples alone, taking into account SNP intermarker distances, SNP-specific heterozygosity rates, and the haplofype structure of the human genome (31) to filter out false-positive LOH. When both parents share the same haplofype, the children will inherit a long stretch of homozygous geneotypes (32). 

Inductive logic programming (ILP) is not a pharmacophore generation method by itself, but a subfield of the machine learning approach. In this field, other methods such as hidden Markov models, Bayesian learning, decision trees and logic programs are available. 

For these reasons, it is important to focus on the most divergent set of superfamily members that can be identified. Although a variety of new methods have recently been developed for identification of distantly related protein sequences [see, for example, Psi-Blast (Altschul et al, 1997), methods based on Hidden Markov Models such as SAMT98 (Kar-plus et al., 1998), the Intermediate Sequence Search algorithm of Park et al., (Park et al., 1997), or the simple congruence method, Shotgun (Pegg and Babbitt, 1999)], confirmation of these relationships can be technically difficult. In some cases, three-dimensional structural information or experimental structure-function analysis will be required to pro-... 

Other variations often appear utilizing different methodologies, but with no relative gain in accuracy. Those include methods that are based on hidden Markov models (Sasagawa and Tagima, 1993), stereo-chemical principles (Lim, 1974) and statistical mechanics (Ptitsyn and Finkelstein, 1983), just to cite a few. 

The detection and diagnosis tasks can be carried out on the process measurements to obtain critical insights into the performance of not only the process itself but also the automatic control system that is deployed to assure normal operation. Today, the integration of such tasks into the process control software associated with Distributed Control Systems (D-CS) is in progress. The technologies continue to advance, especially in the incorporation of multivariate statistics as well as recent developments in signal processing methods such as wavelets and hidden Markov models. 

Other successful structure prediction methods based on sequence alone are FASTA [15, 18], hidden Markov models (HMMs) [25, 26, 28, 179], intermediate sequence search [182], and iterative profile search [29]. Sequence analysis methods are discussed in detail in Chapter 2. The results of such methods in the CASP experiment are described in [157, 183, 184],... 

McClure, M. A., C. Smith, and P. Elton, Parameterization studies for the SAM and HMMER methods of hidden Markov model generation. Ismb, 1996. 4 p. 155-64. 

The regularization procedure takes a different form for each method of statistical learning. When training hidden Markov models, the derived probabilities do not only take the observed sequences into account but also use so-called prior distributions that formulate some hypothesis on the occurrence of output characters in the case that we have no additional information (such as the observed sequences). An expected background distribution of amino acids or nucleotides is a natural starting point for such a prior distribution. 

Recordings from muscle-type nicotinic receptors contain many brief closures [see Figs, ll.lland 11.13 (below)],with a mean lifetime of around 15 ps at 20 C, and because the shortest event that can be detected reliably is around 20-30 ps, the majority of these are missed (because the durations are exponentially distributed, it is possible to estimate the mean even when observations as short as the mean are missed). Methods have improved since then. Now an exact method for allowing for missed events is available, so it is possible to analyze an entire observed recording by maximum likelihood methods that extract all of the information and that incorporate missed event correction. There are some other methods under development, particularly methods based on the theory of Hidden Markov processes, but none are in routine use. 

A mathematically very different approach, which is formally equivalent to the generalized profile method, uses so called Hidden Markov Models (HMMs). A more... 

As has been described in Sect. 5.3, the conservation patterns of enzymes are often indicative of the particular family they belong to and can be used for their classification. However, the iterative searches and multiple alignment methods used for their establishment require a certain bioinformatic infrastructure as well as some experience with these issues. If the goal of the analysis is not the detection of novel enzyme families, but rather the classification of a novel sequence into one of the already existing enzyme families, there are a number of protein domain and motif databases that will be useful in this respect[60 61. These databases do not store the sequences themselves but rather work with descriptors of protein families and protein domains. These descriptors can consist of the Profiles or Hidden Markov Models mentioned above, but other types are also being used. With a particular... 

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