Computer Science Research Works
Permanent URI for this collectionhttp://hdl.handle.net/1903/1593
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Item Method for Identifying Splice Sites and Translational Start Sites in Eukaryotic mRNA(Computer Applications in the Biosciences (CABIOS), 1997) Salzberg, Steven L.This paper describes a new method for determining the consensus sequences that signal the start of donor 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 uncharacterized 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.Item Finding Genes in DNA with a Hidden Markov Model(Journal of Computational Biology, 1997) Henderson, John; Salzberg, Steven; Fasman, Kenneth HThis 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.Item Experiences from an Exploratory Case Study with a Software Risk Management Method(1996-08) Kontio, Jyrki; Englund, Helena; Basili, Victor R.Item The Experience Factory Strategy and Practice(1995-05) Basili, Victor R.; Caldiera, GianluigiItem A Tool for Assisting the Understanding and Formal Development of Software(1993-09-15) Abd-El-Hafiz, S.K.; Basili, Victor R.Item Measuring and Assessing Maintainability at the End of High Level Design(1993-07-25) Briand, Lionel C.; Morasca, Sandro; Basili, Victor R.Item Packaging Reusable Components(1992-09-10) Basili, Victor R.; Abd-El-Hafiz, S.K.Item Software Modeling and Measurement: The Goal/Question/Metric Paradigm(1992-09) Basili, Victor R.Item The Empirical Investigation of Perspective-Based Reading(1995-12) Basili, Victor R.; Green, Scott; Laitenberger, Oliver; Shull, Forrest; Sorumgard, Sivert; Zelkowitz, Marvin V.Item A Pattern-Driven Approach to Code Analysis for Reuse(1991-11) France, R.B.; Basili, Victor R.