Sakakibara, Y., Brown, M., Underwood, R. C., Mian, I. S., and Haussler, D. (1994) In Hunter, L. (ed.), Proceedings of the Twenty-Seventh Annual Hawaii International Conference on System Sciences: Biotechnology Computing, volume V, Los Alamitos, CA: IEEE Computer Society Press. pp. 284--293.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....in each tumor are. Typically, such classiers are trained on a subset of data with a priori given classication and tested on another subset with known classi cation. After assessing the quality of the prediction they can be applied to data the classication of which is unknown. Brown et al. [22] have applied various supervised learning algorithms to six functional classes of yeast genes using gene expression matrices from 79 samples [6] Genes from some of the classes, such as ribosomal proteins and histones, are expected to be co expressed. For these classes a good classication accuracy ....

Brown, M.P.S., Grundy, W.N., Lin, D., Cristianini, N., Sugnet, C.W., Furey, T.S., Ares, M.J. and Haussler, D. (2000) Proc. Natl. Acad. Sci. USA 97, 262^267.

....exist to simultaneously fold and align RNAs (Sankooe 1985; Bafna, Muthukrishnan, Ravi 1995) but they have prohibitive costs in time and, which is worse, in space. In this context, the Covariance Models (CM s) Eddy Durbin 1994) and the more general Stochastic Context Free Grammars (SCFG s) (Sakakibara et al. 1994) look very promising because, once constructed for a given family of sequences, they allow one to discriminate, fold and align new sequences belonging to this family. While an actual procedure has been given to automatically construct a CM from a set of unaligned, unfolded, homologous sequences, ....

....the interpretation of the high level description of the rst tape is only a function of the MTSAG. Thus it is easy to document and change this interpretation. We shall give later an example of such a change. To illustrate this point we transformed a SCFG used by Haussler s team to model tRNAs (Sakakibara et al. 1994). This grammar has 97 nonterminals and 660 rules, and it is quite hard to read and understand directly. Once turned into a high level description suitable for the rst tape of a MTSAG, it looked like the rst tape of the 2 tape input string of gure 5(a) The MTSAG which was used to iinterpretj ....

Sakakibara, Y.; Brown, M.; Hughey, R.; Mian, I. S.; Sj#- lander, K.; Underwood, R. C.; and Haussler, D. 1994.

....a simpler and more effective approach [24, 25] Our method is closely related to the covariance models (CMs) of Eddy and Durbin [26] CMs are equivalent to SCFGs but the algorithms for training and producing multiple alignments differ. An in depth comparison of the two methods is given elsewhere [27]. In this paper, we focus on validating the use of SCFGs to model RNA by applying them to tRNA. We create a statistical model of tRNA by generating a SCFG incorporating base pairing information in a manner similar to our construction of an HMM representing a statistical model of a protein family ....

....3) For the discrimination experiments, we generated 2016 non tRNA test sequences from the non tRNA features (including mRNA, rRNA, and CDS) in NewGenBank 75.0 and GenBank75.0. We created 20 non tRNA sequences for each sequence length between 20 to 120 bases. 3 The Tree Grammar EM algorithm [27, 24] was used to reestimate the probability parameters in this initial grammar using varying numbers of unfolded and unaligned tRNA training sequences (Figure 3) The run time for training was around 30 CPU minutes for 100 training sequences on a Sun Sparcstation 10 30 for a single step through the ....

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Sakakibara, Y., Brown, M., Hughey, R., Mian, I. S., Sjolander, K., Underwood, R. C., and Haussler, D. (1994) In Proceedings of the Asilomar Conference on Combinatorial Pattern Matching New York, NY: Springer-Verlag. In press.

.... the grammar from training sequences in the form of costs and other trainable parameters used during parsing [21, 22, 23] In our work, we have developed an integrated probabilistic framework for estimating the parameters of an SCFG which may prove to be a simpler and more effective approach [24, 25]. Our method is closely related to the covariance models (CMs) of Eddy and Durbin [26] CMs are equivalent to SCFGs but the algorithms for training and producing multiple alignments differ. An in depth comparison of the two methods is given elsewhere [27] In this paper, we focus on validating ....

....within larger sequences. Durbin and Eddy have implemented the latter modifications in their tRNA experiments and report good results in searching the GenBank structural RNA database and 2.2 Mb of C. elegans genomic sequence for tRNAs, even without using special intron models. In our earlier work [25], we reported very preliminary results on modifying tRNA grammars to accommodate introns. We see no insurmountable obstacles in developing effective stochastic grammar based search methods, but predict that the main practical problem will be dealing with Number of Sequences with Z Scores Above 5 ....

Sakakibara, Y., Brown, M., Mian, I. S., Underwood, R., and Haussler, D. (1994) In Proceedings of the Hawaii International Conference on System Sciences Los Alamitos, CA: IEEE Computer Society Press. .

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Sakakibara, Y., Brown, M., Mian, I. S., Underwood, R., and Haussler, D. (1994) In Proceedings of the Hawaii International Conference on System Sciences Los Alamitos, CA: IEEE Computer Society Press.

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Sakakibara, Y., Brown, M., Hughey, R., Mian, I. S., Sj¨olander, K., Underwood, R. C., and Haussler, D. (1994) In Proceedings of the Asilomar Conference on Combinatorial Pattern Matching New York, NY: Springer-Verlag. In press.

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Sakakibara, Y., Brown, M., Underwood, R. C., Mian, I. S., and Haussler, D. (1994) In Hunter, L. (ed.), Proceedings of the Twenty-Seventh Annual Hawaii International Conference on System Sciences: Biotechnology Computing, volume V, Los Alamitos, CA: IEEE Computer Society Press. pp. 284--293.

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Krogh, A., Brown, M., Mian, I. S., Sjolander, K., and Haussler, D. (1994) J. Mol. Biol. 235, 1501--1531.

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Brown, M.P., Grundy, W.N., Lin, D., Cristianini, N., Sugnet, C.W., Furey, T.S., Ares, M., Jr., and Haussler, D. (2000) Proc. Natl. Acad. Sci. USA 97: 262-267.

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Brown,M., Hughey,R., Krogh,A., Mian,I.S., Sjlander,K. and Haussler,D. (1993) In Hunter,L., Searls,D. and Shavlik,J. (eds.), Proceedings of the First International Conference on Intelligent Systems for Molecular Biology. AAAI Press, Menlo Park, CA, pp. 47--55.

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Krogh,A., Brown,M., Mian,I.S., Sjolander,K. and Haussler,D.J. (1994) J. Mol. Biol., 235, 1501--1531.

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Krogh, A., Brown, M., Mian, I.S., Sjolander, K. and Haussler, D. (1994) J. Mol. Biol., 235, 1501--1531.

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Krogh,A., Brown,M., Mian,I.S., Sjoelander,K. and Haussler,D. (1994) J. Mol. Biol., 235, 1501-1531.

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Krogh,A., Brown,M., Mian,I.S., Sjoelander,K. and Haussler,D. (1994) J. Mol. Biol., 235, 1501--1531.

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Krogh, A., Brown, M., Mian, I. S., Sjolander, K. and Haussler, D. (1994) J. Mol. Biol., 235, 15011531.

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