Above are papers related to the following paper: [Paper_ID] Paper #1154520. 	[Title] that fits the asymptotics of the problem. References [Abstract] 1] D. Aldous and P. Shields. A diffusion limit for a class of randomly growing binary trees. Probability Theory, 79:509-542, 1988. [2] R. Breathnach, C. Benoist, K. O'Hare, F. Gannon, and P. Chambon. Ovalbumin gene: Evidence for leader sequence in mRNA and DNA sequences at the exon-intron boundaries. Proceedings of the National Academy of Science, 75:4853-4857, 1978. [3] S. Brunak, J. Engelbrecht, and S. Knudsen. Prediction of human mRNA donor and acceptor sites from the DNA sequence. Journal of Molecular Biology, 220:49, 1991. [4] Jack Cophen and Ian Stewart. The information in your hand. The Mathematical Intelligencer, 13(3), 1991. [5] R. G. Gallager. Information Theory and Reliable Communication. John Wiley & Sons, Inc., 1968. [6] Ali Hariri, Bruce Weber, and John Olmstead. On the validity of Shannon-information calculations for molecular biological sequence. Journal of Theoretical Biology, 147:235-254, 1990. [7] W. B. Davenport Jr. and W. L. Root. An Introduction to the Theory of Random Signals and Noise. McGraw-Hill, 1958. [8] Andrzej Knopka and John Owens. Complexity charts can be used to map functional domains in DNA. Gene Anal. Techn., 6, 1989. [9] S.M. Mount. A catalogue of splice-junction sequences. Nucleic Acids Research, 10:459-472, 1982. [10] H.M. Seidel, D.L. Pompliano, and J.R. Knowles. Exons as microgenes? Science, 257, September 1992. [11] C. E. Shannon. A mathematical theory of communication. Bell System Tech. J., 27:379-423, 623-656, 1948. [12] Peter S. Shenkin, Batu Erman, and Lucy D. Mastrandrea. Information-theoretical entropy as a measure of sequence variability. Proteins, 11(4):297, 1991. [13] R. Staden. Measurements of the effects that coding for a protein has on a DNA sequence and their use for finding genes. Nucleic Acids Research, 12:551-567, 1984. [14] J.A. Steitz. Snurps. Scientific American, 258(6), June 1988. [15] H. van Trees. Detection, estimation and modulation theory. Wiley, 1971. [16] J. D. Watson, N. H. Hopkins, J. W. Roberts, J. Ar-getsinger Steitz, and A. M. Weiner. Molecular Biology of the Gene. Benjamin/Cummings, Menlo Park, CA, fourth edition, 1987. [17] A.D. Wyner and A.J. Wyner. An improved version of the Lempel-Ziv algorithm. Transactions of Information Theory. [18] A.J. Wyner. String Matching Theorems and Applications to Data Compression and Statistics. PhD thesis, Stanford University, 1993. [19] J. Ziv and A. Lempel. A universal algorithm for sequential data compression. IEEE Transactions on Information Theory, IT-23(3):337-343, 1977. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1128868. 	[Title] Identification of Protein Coding Regions In Genomic DNA Molecular, Cellular and Developmental Biology, Keywords: gene [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1129572. 	[Title] Non-Deterministic, Constraint-Based Parsing of Human Genes [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #215912. 	[Title] A Method for Identifying Splice Sites and Translational Start Sites in [Abstract] This paper describes a new method for determining the consensus sequences that signal the start of translation and the boundaries between exons and introns (donor and acceptor sites) in eukaryotic mRNA. The method takes into account the dependencies between adjacent bases, in contrast to the usual technique of considering each position independently. When coupled with a dynamic program to compute the most likely sequence, new consensus sequences emerge. The consensus sequence information is summarized in conditional probability matrices which, when used to locate signals in uncharacter-ized genomic DNA, have greater sensitivity and specificity than conventional matrices. Species-specific versions of these matrices are especially effective at distinguishing true and false sites. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #126128. 	[Title] DNA Sequence Classification Using Compression-Based Induction [Abstract] DIMACS Technical Report 95-04 April 1995 Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #194609. 	[Title] Prediction of human mRNA donor and acceptor sites from the DNA sequence [Abstract] Artificial neural networks have been applied to the prediction of splice site location in human pre-mRNA. A joint prediction scheme where prediction of transition regions between introns and exons regulates a cutoff level for splice site assignment was able to predict splice site locations with confidence levels far better than previously reported in the literature. The problem of predicting donor and acceptor sites in human genes is hampered by the presence of numerous amounts of false positives | in the paper the distribution of these false splice sites is examined and linked to a possible scenario for the splicing mechanism in vivo. When the presented method detects 95% of the true donor and acceptor sites it makes less than 0.1% false donor site assignments and less than 0.4% false acceptor site assignments. For the large data set used in this study this means that on the average there are one and a half false donor sites per true donor site and six false acceptor sites per true acceptor site. With the joint assignment method more than a fifth of the true donor sites and around one fourth of the true acceptor sites could be detected without accompaniment of any false positive predictions. Highly confident splice sites could not be isolated with a widely used weight matrix method or by separate splice site networks. A complementary relation between the confidence levels of the coding/non-coding and the separate splice site networks was observed, with many weak splice sites having sharp transitions in the coding/non-coding signal and many stronger splice sites having more ill-defined transitions between coding and non-coding. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #31927. 	[Title] A Generalized Hidden Markov Model for the Recognition of Human Genes in DNA [Abstract] We present a statistical model of genes in DNA. A Generalized Hidden Markov Model (GHMM) provides the framework for describing the grammar of a legal parse of a DNA sequence (Stormo & Haussler 1994). Probabilities are assigned to transitions between states in the GHMM and to the generation of each nucleotide base given a particular state. Machine learning techniques are applied to optimize these probabilities using a standardized training set. Given a new candidate sequence, the best parse is deduced from the model using a dynamic programming algorithm to identify the path through the model with maximum probability. The GHMM is flexible and modular, so new sensors and additional states can be inserted easily. In addition, it provides simple solutions for integrating cardinality constraints, reading frame constraints, "indels", and homology searching. The description and results of an implementation of such a gene-finding model, called Genie, is presented. The exon sensor is a codon frequency model conditioned on windowed nucleotide frequency and the preceding codon. Two neural networks are used, as in (Brunak, Engelbrecht, & Knudsen 1991), for splice site prediction. We show that this simple model performs quite well. For a cross-validated standard test set of 304 genes [ftp://www-hgc.lbl.gov/pub/genesets] in human DNA, our gene-finding system identified up to 85% of protein-coding bases correctly with a specificity of 80%. 58% of exons were exactly identified with a specificity of 51%. Genie is shown to perform favorably compared with several other gene-finding systems. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #563613. 	[Title] Gene Structure Prediction by Linguistic Methods [Abstract] The higher-order structure of genes and other features of biological sequences can be described by means of formal grammars. These grammars can then be used by general-purpose parsers to detect and assemble such structures by means of syntactic pattern recognition. We describe a grammar and parser for eukaryotic protein-encoding genes, which by some measures is as effective as current connectionist and combinatorial algorithms in predicting gene structures for sequence database entries. Parameters on the grammar rules are optimized for several different species, and mixing experiments performed to determine the degree of species specificity and the relative importance of compositional, signal-based, and syntactic components in gene prediction. Please summarise all the given information.

Paper #1154520 has the title `that fits the asymptotics of the problem. References  
` and the abstract `1] D. Aldous and P. Shields. A diffusion limit for a class of randomly growing binary trees. Probability Theory, 79:509-542, 1988. [2] R. Breathnach, C. Benoist, K. O'Hare, F. Gannon, and P. Chambon. Ovalbumin gene: Evidence for leader sequence in mRNA and DNA sequences at the exon-intron boundaries. Proceedings of the National Academy of Science, 75:4853-4857, 1978. [3] S. Brunak, J. Engelbrecht, and S. Knudsen. Prediction of human mRNA donor and acceptor sites from the DNA sequence. Journal of Molecular Biology, 220:49, 1991. [4] Jack Cophen and Ian Stewart. The information in your hand. The Mathematical Intelligencer, 13(3), 1991. [5] R. G. Gallager. Information Theory and Reliable Communication. John Wiley & Sons, Inc., 1968. [6] Ali Hariri, Bruce Weber, and John Olmstead. On the validity of Shannon-information calculations for molecular biological sequence. Journal of Theoretical Biology, 147:235-254, 1990. [7] W. B. Davenport Jr. and W. L. Root. An Introduction to the Theory of Random Signals and Noise. McGraw-Hill, 1958. [8] Andrzej Knopka and John Owens. Complexity charts can be used to map functional domains in DNA. Gene Anal. Techn., 6, 1989. [9] S.M. Mount. A catalogue of splice-junction sequences. Nucleic Acids Research, 10:459-472, 1982. [10] H.M. Seidel, D.L. Pompliano, and J.R. Knowles. Exons as microgenes? Science, 257, September 1992. [11] C. E. Shannon. A mathematical theory of communication. Bell System Tech. J., 27:379-423, 623-656, 1948. [12] Peter S. Shenkin, Batu Erman, and Lucy D. Mastrandrea. Information-theoretical entropy as a measure of sequence variability. Proteins, 11(4):297, 1991. [13] R. Staden. Measurements of the effects that coding for a protein has on a DNA sequence and their use for finding genes. Nucleic Acids Research, 12:551-567, 1984. [14] J.A. Steitz. Snurps. Scientific American, 258(6), June 1988. [15] H. van Trees. Detection, estimation and modulation theory. Wiley, 1971. [16] J. D. Watson, N. H. Hopkins, J. W. Roberts, J. Ar-getsinger Steitz, and A. M. Weiner. Molecular Biology of the Gene. Benjamin/Cummings, Menlo Park, CA, fourth edition, 1987. [17] A.D. Wyner and A.J. Wyner. An improved version of the Lempel-Ziv algorithm. Transactions of Information Theory. [18] A.J. Wyner. String Matching Theorems and Applications to Data Compression and Statistics. PhD thesis, Stanford University, 1993. [19] J. Ziv and A. Lempel. A universal algorithm for sequential data compression. IEEE Transactions on Information Theory, IT-23(3):337-343, 1977. 
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Question: Which category should Paper #1154520 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1154520 has the title `that fits the asymptotics of the problem. References  
` and the abstract `1] D. Aldous and P. Shields. A diffusion limit for a class of randomly growing binary trees. Probability Theory, 79:509-542, 1988. [2] R. Breathnach, C. Benoist, K. O'Hare, F. Gannon, and P. Chambon. Ovalbumin gene: Evidence for leader sequence in mRNA and DNA sequences at the exon-intron boundaries. Proceedings of the National Academy of Science, 75:4853-4857, 1978. [3] S. Brunak, J. Engelbrecht, and S. Knudsen. Prediction of human mRNA donor and acceptor sites from the DNA sequence. Journal of Molecular Biology, 220:49, 1991. [4] Jack Cophen and Ian Stewart. The information in your hand. The Mathematical Intelligencer, 13(3), 1991. [5] R. G. Gallager. Information Theory and Reliable Communication. John Wiley & Sons, Inc., 1968. [6] Ali Hariri, Bruce Weber, and John Olmstead. On the validity of Shannon-information calculations for molecular biological sequence. Journal of Theoretical Biology, 147:235-254, 1990. [7] W. B. Davenport Jr. and W. L. Root. An Introduction to the Theory of Random Signals and Noise. McGraw-Hill, 1958. [8] Andrzej Knopka and John Owens. Complexity charts can be used to map functional domains in DNA. Gene Anal. Techn., 6, 1989. [9] S.M. Mount. A catalogue of splice-junction sequences. Nucleic Acids Research, 10:459-472, 1982. [10] H.M. Seidel, D.L. Pompliano, and J.R. Knowles. Exons as microgenes? Science, 257, September 1992. [11] C. E. Shannon. A mathematical theory of communication. Bell System Tech. J., 27:379-423, 623-656, 1948. [12] Peter S. Shenkin, Batu Erman, and Lucy D. Mastrandrea. Information-theoretical entropy as a measure of sequence variability. Proteins, 11(4):297, 1991. [13] R. Staden. Measurements of the effects that coding for a protein has on a DNA sequence and their use for finding genes. Nucleic Acids Research, 12:551-567, 1984. [14] J.A. Steitz. Snurps. Scientific American, 258(6), June 1988. [15] H. van Trees. Detection, estimation and modulation theory. Wiley, 1971. [16] J. D. Watson, N. H. Hopkins, J. W. Roberts, J. Ar-getsinger Steitz, and A. M. Weiner. Molecular Biology of the Gene. Benjamin/Cummings, Menlo Park, CA, fourth edition, 1987. [17] A.D. Wyner and A.J. Wyner. An improved version of the Lempel-Ziv algorithm. Transactions of Information Theory. [18] A.J. Wyner. String Matching Theorems and Applications to Data Compression and Statistics. PhD thesis, Stanford University, 1993. [19] J. Ziv and A. Lempel. A universal algorithm for sequential data compression. IEEE Transactions on Information Theory, IT-23(3):337-343, 1977. 
`.
Question: Which category should Paper #1154520 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Neural_Networks