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Test Data Sets and Evaluation of Gene Prediction Programs on the Rice Genome

Journal of Computer Science & Technology 2005年第4期20卷 446-453页

作者：李恒刘劲松徐昭金蛟方林高雷李余动邢自兴高绍根刘涛李海红李雁谢惠民郑伟谋郝柏林 BeijingGenomicsInstitute（BG1） AcademiaSinicaBeijing101300P.R.China／／~InstituteofTheoreticalPhysicsAcademiaSinicaBeijing100080P.R.China BeijingGenomicsInstitute（BG1） AcademiaSinicaBeijing101300P.R.China BeijingGenomicsInstitute（BG1） AcademiaSinicaBeijing101300P.R.China／／DepartmentofMathematicsBeijingNormalUniversityBeijing100875P.R.China BeijingGenomicsInstitute（BG1） AcademiaSinicaBeijing101300P.R.China／／InstituteofSystemsScienceAcademiaSinicaBeijing100080P.R.China HangzhouBranchofBGI Hangzhou310008P.R.China DepartmentofMathematics SuzhouUniversitySuzhou215006P.R.China InstituteofTheoreticalPhysics AcademiaSinicaBeijing100080P.R.China／／HangzhouBranchofBGIHangzhou310008P.R.China／／T-LifeResearchCenterFudanUniversityShanghai200433P.R.China

With several rice genome projects approaching completion gene prediction/finding by computer algorithms has become an urgent task. Two test sets were constructed by mapping the newly published 28,469 full-length KOME rice cDNA to the RGP BAC clone sequences of Oryza sativa ssp. japonica: a single-gene set of 550 sequences and a multi-gene set of 62 sequences with 271 genes. These data sets were used to evaluate five ab initio gene prediction programs: RiceHMM,GlimmerR, GeneMark, FGENSH and BGF. The predictions were compared on nucleotide, exon and whole gene structure levels using commonly accepted measures and several new measures. The test results show a progress in performance in chronological order. At the same time complementarity of the programs hints on the possibility of further improvement and on the feasibility of reaching better performance by combining several gene-finders.

关键词： gene prediction rice genome test sets accuracy measures hidden Markov models dynamic programming

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Comparison of Two Time Series Decomposition Methods: Least Squares and Buys-Ballot Methods

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Open Journal of Statistics 2016年第6期6卷 1123-1137页

作者： I. S. Iwueze E. C. Nwogu V. U. Nlebedim J. C. Imoh Department of Statistics Federal University of Technology Owerri Nigeria Dept of Maths/Statistics Imo State Polytechnic Umuagwo Nigeria

This paper discusses comparison of two time series decomposition methods: The Least Squares Estimation (LSE) and Buys-Ballot Estimation (BBE) methods. As noted by Iwueze and Nwogu (2014), there exists a research gap for the choice of appropriate model for decomposition and detection of presence of seasonal effect in a series model. Estimates of trend parameters and seasonal indices are all that are needed to fill the research gap. However, these estimates are obtainable through the Least Squares Estimation (LSE) and Buys-Ballot Estimation (BBE) methods. Hence, there is need to compare estimates of the two methods and recommend. The comparison of the two methods is done using the accuracy measures (Mean Error (ME)), Mean Square Error (MSE), the Mean Absolute Error (MAE), and the Mean Absolute Percentage Error (MAPE). The results from simulated series show that for the additive model;the summary statistics (ME, MSE and MAE) for the two estimation methods and for all the selected trending curves are equal in all the simulations both in magnitude and direction. For the multiplicative model, results show that when a series is dominated by trend, the estimates of the parameters by both methods become less precise and differ more widely from each other. However, if conditions for successful transformation (using the logarithmic transform in linearizing the multiplicative model to additive model) are met, both of them give similar results.

关键词： Decomposition Models Least Squares Estimates Buys-Ballot Estimates accuracy measures Successful Transformation Trending Curves

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Notes on Rough Set Approximations and Associated measures

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浙江海洋学院学报（自然科学版） 2010年第5期29卷 399-410页

作者： YAO Yiyu Department of Computer Science University of Regina Regina Saskatchewan Canada S4S 0A2

We review and compare two definitions of rough set *** is defined by a pair of sets in the universe and the other by a pair of sets in the quotient *** latter definition,although less studied,is semantically superior for interpreting rule induction and is closely related to granularity switching in granular *** measures about the accuracy and quality of approximations are *** semantics difficulties are commented.

关键词： accuracy measures Granularity Rough set approximations Roughness

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