Hidden Markov Models: Methods and Protocols (Methods in by David R. Westhead, M. S. Vijayabaskar

By David R. Westhead, M. S. Vijayabaskar

This quantity goals to supply a brand new standpoint at the broader utilization of Hidden Markov versions (HMMs) in biology. Hidden Markov versions: equipment and Protocols courses readers via chapters on organic structures; starting from unmarried biomolecule, mobile point, and to organism point and using HMMs in unravelling the advanced mechanisms that govern those advanced systems.  Written within the hugely profitable Methods in Molecular Biology sequence layout, chapters comprise introductions to their respective issues, lists of the required fabrics and reagents, step by step, effectively reproducible laboratory protocols, and tips about troubleshooting and heading off identified pitfalls.

Authoritative and functional, Hidden Markov versions: equipment and Protocols goals to illustrate the effect of HMM in biology and encourage new research.

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Akaike H, Company NP (1981) Likelihood of a model and information criteria. J Econom 16:3–14 36. Schwarz G (1978) Estimating the dimension of a model. Ann Math Stat 6:461–464 37. Keller BG, Kobitski AY, Jaeschke A, Nienhaus GU, Noe F (2014) Complex RNA folding kinetics revealed by single molecule FRET and hidden Markov models. J Am Chem Soc 136:4534–4543 Chapter 4 Predicting Beta Barrel Transmembrane Proteins Using HMMs Georgios N. Tsaousis, Stavros J. Hamodrakas, and Pantelis G. Bagos Abstract Transmembrane beta-barrels (TMBBs) constitute an important structural class of membrane proteins located in the outer membrane of gram-negative bacteria, and in the outer membrane of chloroplasts and mitochondria.

5 Notes 1. Estimation of HMM parameters (transition matrix, emission probability distribution, and initial state probabilities) is done by expectation-maximization (EM). Expectation maximization is done in two steps. The forward–backward algorithm is applied in the E-step. In the M-step, a modified log likelihood function, which is penalized for L1 regularization, is maximized to estimate HMM parameters. Detailed balance is also enforced during the M-step in order to satisfy the second law of thermodynamics.

3. The scores for each fold and for each protein of the training set were entered into the optimization procedure, along with the initial parameters (which are all set equal to 1). The optimization procedure identifies the optimal parameters, which are multiplied with the scores of the test set proteins against the 3-HMM. The final scores were used for the fold classification of the test proteins. It should be noted that the training proteins correspond to the primary and the true secondary structure of the proteins, while the test proteins correspond to the primary and the predicted secondary structure of the test proteins.

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