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.

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 #1124844. 	[Title] Stochastic Decomposition of DNA Sequences Using Hidden Markov Models [Abstract] This work presents an application of a machine learning for characterizing an important property of natural DNA sequences compositional inhomogeneity. Compositional segments often correspond to meaningful biological units. Taking into account such inhomogeneity is a prerequisite of successful recognition of functional features in DNA sequences, especially, protein-coding genes. Here we present a technique for DNA segmentation using hidden Markov models. A DNA sequence is represented by a chain of homogeneous segments, each described by one of a few statistically discriminated hidden states, whose contents form a first-order Markov chain. The technique is used to describe and compare chromosomes I and IV of the completely sequenced Saccharomyces cerevisiae (yeast) genome. Our results indicate the existence of a few well separated states, which gives support to the isochore theory. We also explore the model's likelihood landscape and analyze the dynamics of the optimization process, thus addressing the problem of reliability of the obtained optima and efficiency of the algorithms. 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 #377303. 	[Title] Evolving Deterministic Finite Automata Using Cellular Encoding programming and cellular encoding. Programs are evolved that [Abstract] This paper presents a method for the initial singlestate zygote. The results evolution of deterministic finite Please summarise all the given information.

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 #31353. 	[Title] Hidden Markov Models in Computational Biology: Applications to Protein Modeling UCSC-CRL-93-32 Keywords: Hidden Markov Models, [Abstract] Hidden Markov Models (HMMs) are applied to the problems of statistical modeling, database searching and multiple sequence alignment of protein families and protein domains. These methods are demonstrated on the globin family, the protein kinase catalytic domain, and the EF-hand calcium binding motif. In each case the parameters of an HMM are estimated from a training set of unaligned sequences. After the HMM is built, it is used to obtain a multiple alignment of all the training sequences. It is also used to search the SWISS-PROT 22 database for other sequences that are members of the given protein family, or contain the given domain. The HMM produces multiple alignments of good quality that agree closely with the alignments produced by programs that incorporate three-dimensional structural information. When employed in discrimination tests (by examining how closely the sequences in a database fit the globin, kinase and EF-hand HMMs), the HMM is able to distinguish members of these families from non-members with a high degree of accuracy. Both the HMM and PRO-FILESEARCH (a technique used to search for relationships between a protein sequence and multiply aligned sequences) perform better in these tests than PROSITE (a dictionary of sites and patterns in proteins). The HMM appears to have a slight advantage Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #31932. 	[Title] A Decision Tree System for Finding Genes in DNA [Abstract] MORGAN is an integrated system for finding genes in vertebrate DNA sequences. MORGAN uses a variety of techniques to accomplish this task, the most distinctive of which is a decision tree classifier. The decision tree system is combined with new methods for identifying start codons, donor sites, and acceptor sites, and these are brought together in a frame-sensitive dynamic programming algorithm that finds the optimal segmentation of a DNA sequence into coding and noncoding regions (exons and introns). The optimal segmentation is dependent on a separate scoring function that takes a subsequence and assigns to it a score reflecting the probability that the sequence is an exon. The scoring functions in MORGAN are sets of decision trees that are combined to give a probability estimate. Experimental results on a database of 570 vertebrate DNA sequences show that MORGAN has excellent performance by many different measures. On a separate test set, it achieves an overall accuracy of 95%, with a correlation coefficient of 0.78 and a sensitivity and specificity for coding bases of 83% and 79%. In addition, MORGAN identifies 58% of coding exons exactly; i.e., both the beginning and end of the coding regions are predicted correctly. This paper describes the MORGAN system, including its decision tree routines and the algorithms for site recognition, and its performance on a benchmark database of vertebrate DNA. 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 #194617. 	[Title] Finding Genes in DNA with a Hidden Markov Model [Abstract] This study describes a new Hidden Markov Model (HMM) system for segmenting uncharacterized genomic DNA sequences into exons, introns, and intergenic regions. Separate HMM modules were designed and trained for specific regions of DNA: exons, introns, intergenic regions, and splice sites. The models were then tied together to form a biologically feasible topology. The integrated HMM was trained further on a set of eukaryotic DNA sequences, and tested by using it to segment a separate set of sequences. The resulting HMM system, which is called VEIL (Viterbi Exon-Intron Locator), obtains an overall accuracy on test data of 92% of total bases correctly labelled, with a correlation coefficient of 0.73. Using the more stringent test of exact exon prediction, VEIL correctly located both ends of 53% of the coding exons, and 49% of the exons it predicts are exactly correct. These results compare favorably to the best previous results for gene structure prediction, and demonstrate the benefits of using HMMs for this problem. Please summarise all the given information.

Paper #563613 has the title `Gene Structure Prediction by Linguistic Methods  
` and the 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.
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Question: Which category should Paper #563613 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #563613 has the title `Gene Structure Prediction by Linguistic Methods  
` and the 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.
`.
Question: Which category should Paper #563613 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