Stultz, C. M.; White, J. V.; and Smith, T. F. 1993. Structural analysis based on state-space modeling.Protein Science 2:305#315.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....David Haussler, and co workers at UC Santa Cruz introduced a form of HMM which is well suited to protein and DNA sequence analysis [10] adopting HMM techniques which have been used for years in speech recognition. HMMs had been used in biology before notably, for modeling protein structure [16] but the Krogh paper had a particularly dramatic impact. Since then, several computational biology groups (including ours) have rapidly adopted HMMs as the underlying formalism for how to deal with problems involving primary sequence consensus, such as multiple sequence alignment and sensitive ....

Collin M. Stultz, James V. White, and Temple F. Smith. Structural analysis based on state-space modeling. Protein Sci., 2:305--314, 1993.

....be applied to Bayesian networks representing knowledge about protein foldings. More precisely, we are interested in statistical modeling of protein folding motifs ( invariant parts in known 3D protein structures important for predicting unknown foldings ) This is related to research described in [9] [10] 11] To summarize, the main thrusts of this project are : CNRS Research Project Proposal 4 ffl A new knowledge representation formalism ( Bayesian networks ) combining the explicit character of symbolic formalisms with computational power of numerical ones ; This is a new problem for ....

....This promising line of research in machine learning needs further investigation. ffl Challenging real world application demanding Bayesian network based representation ; The stuck problem of protein structure prediction seems to get easier under a recent push of statistical modeling methods [9] [10] 11] The rest of this proposal is organized as follows. In Section 2 we discuss Bayesian networks as knowledge representation formalism and compare them with neural networks and classification trees. In Section 3 we discuss in details the learning of Bayesian networks. We present Bayesian ....

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Stultz C.M., White J.V., Smith T.F. (1993) Structural analysis based on state-space modeling, Protein Science, 2, 305-314.

....applications of HMMs and the EM (Expectation Maximization) algorithm, including our own, presage a more widespread use of this technique in computational biology. During the time that we have been developing this approach, several related efforts have come to our attention. One is that of White, Stultz and Smith (1991) 1993), who use HMMs to model protein superfamilies. This work is more ambitious than our own, since superfamilies are harder to characterize than families. It is not yet clear how successful their work has been since no results are reported for sequences not in the training set. If there are weaknesses ....

....an insertion or deletion is more likely than starting one. This choice appears to have worked well for modeling the protein families that we have examined, but other types of HMMs may be better at other tasks, e.g. the more elaborate models for protein superfamilies used in (White et al. 1991; Stultz et al. 1993). The important feature of the HMM method is its generality. One can choose any structure for the states and transitions that is appropriate for the problem at hand. Examples of more general HMM architectures are given in Sections 2.4 and 2.5 below. 2.2 Estimating the parameters of an HMM from ....

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Stultz, C. M., White, J. V., & Smith, T. F. (1993). Structural analysis based on state-space modeling. Protein Science, 2, 305--315.

.... statistical models, related to profiles (Waterman and Perlwitz, 1986; Barton and Sternberg, 1990; Gribskov et al. 1990; Bowie et al. 1991; Luthy et al. 1991) that can be successfully applied to the problems of modeling protein and nucleic acid families (Churchill, 1989; White et al. 1991; Stultz et al. 1993; Krogh et al. 1992; Hughey, 1993; Baldi et al. 1992; Baldi and Chauvin, 1993; Asai, K. and Hayamizu, S. and Onizuka, K. 1993) HMMs can be extremely effective for database searching and, without the aid of three dimensional structural information, can in some z This work was supported in ....

Stultz, C.M.; White, J. V.; and Smith, T.F. 1993. Structural analysis based on state-space modeling. Protein Science 2:305--315.

.... Scheraga, 1979; Zhang et al. 1992; Stolorz et al. 1992; Goldstein et al. 1994) Such ideas, however, have been used in the probabilistic modeling of inter residue correlations in the EF hand motif (Mamitsuka, 1995) and they are an implicit feature of hidden Markov models (Asai et al. 1993; Stultz et al. 1993; Krogh et al. 1994) Structure Descriptors Equation 6 implies that the identity of an amino acid in the sequence will be determined only by the secondary structure at that location. In fact, other factors, such as surface accessibility, will be major influences. It has been observed that ....

Stultz CM, White JV, and Smith TF. 1993. Structural analysis based on state-space modeling. Protein Science 2:305--314.

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Stultz, C.M., J.V. White, and T.F. Smith. 1993. Structural analysis based on state-space modeling . Protein Science 2:305-14.

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Stultz, C. M.; White, J. V.; and Smith, T. F. 1993. Structural analysis based on state-space modeling.Protein Science 2:305#315.

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Stultz,C.M., White,J.V. and Smith,T.F. (1993) Structural analysis based on state-space modeling. Protein Sci., 2, 305--314.

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JMB 216:813--818. Stultz, C. M.; White, J. V.; and Smith, T. F. 1993. Structural analysis based on state-space modeling. Protein Science 2:305--315.

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JMB 216:813--818. Stultz, C. M.; White, J. V.; and Smith, T. F. 1993. Structural analysis based on state-space modeling. Protein Science 2:305--315.

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