Feature Selection for Classification of Nucleic Acid Sequences

[1]  Yvan Saeys,et al.  Feature Ranking Using an EDA-based Wrapper Approach , 2006, Towards a New Evolutionary Computation.

[2]  Yvan Saeys,et al.  Digging into Acceptor Splice Site Prediction: An Iterative Feature Selection Approach , 2004, PKDD.

[3]  Pavel Laskov,et al.  Feasible Direction Decomposition Algorithms for Training Support Vector Machines , 2002, Machine Learning.

[4]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[5]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[6]  Yvan Saeys,et al.  Selecting relevant features for gene structure prediction , 2004 .

[7]  S. Salzberg,et al.  Computational gene finding in plants , 2004, Plant Molecular Biology.

[8]  Yvan Saeys,et al.  Feature selection for splice site prediction: A new method using EDA-based feature ranking , 2004, BMC Bioinformatics.

[9]  Yvan Saeys,et al.  Fast feature selection using a simple estimation of distribution algorithm: a case study on splice site prediction , 2003, ECCB.

[10]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[11]  Bernhard Schölkopf,et al.  Use of the Zero-Norm with Linear Models and Kernel Methods , 2003, J. Mach. Learn. Res..

[12]  P. Rouzé,et al.  Current methods of gene prediction, their strengths and weaknesses. , 2002, Nucleic acids research.

[13]  Bernard De Baets,et al.  Feature subset selection for splice site prediction , 2002, ECCB.

[14]  Michael Q. Zhang Computational prediction of eukaryotic protein-coding genes , 2002, Nature Reviews Genetics.

[15]  Gunnar Rätsch,et al.  New Methods for Splice Site Recognition , 2002, ICANN.

[16]  Marko Grobelnik,et al.  Feature Selection Using Linear Support Vector Machines , 2002 .

[17]  Artemis G. Hatzigeorgiou,et al.  Translation initiation start prediction in human cDNAs with high accuracy , 2002, Bioinform..

[18]  Yvan Saeys,et al.  Selecting Relevant Features for Splice Site Prediction by Estimation of Distribution Algorithms. , 2002 .

[19]  D. Brow,et al.  Allosteric cascade of spliceosome activation. , 2002, Annual review of genetics.

[20]  Limsoon Wong,et al.  Using feature generation and feature selection for accurate prediction of translation initiation sites. , 2002, Genome informatics. International Conference on Genome Informatics.

[21]  J. A. Lozano,et al.  Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation , 2001 .

[22]  S. Salzberg,et al.  GeneSplicer: a new computational method for splice site prediction. , 2001, Nucleic acids research.

[23]  B. Graveley Alternative splicing: increasing diversity in the proteomic world. , 2001, Trends in genetics : TIG.

[24]  Dimitris Anastassiou,et al.  Frequency-domain analysis of biomolecular sequences , 2000, Bioinform..

[25]  Thomas Schiex,et al.  EUGÈNE: An Eukaryotic Gene Finder That Combines Several Sources of Evidence , 2000, JOBIM.

[26]  W. Filipowicz,et al.  Pre-mRNA splicing in higher plants. , 2000, Trends in plant science.

[27]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[28]  Yvan Saeys,et al.  A Study and Improvement of the Genetic Algorithm in the CAM-Brain Machine , 2000 .

[29]  Mineichi Kudo,et al.  Comparison of algorithms that select features for pattern classifiers , 2000, Pattern Recognit..

[30]  Anil K. Jain,et al.  Statistical Pattern Recognition: A Review , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[31]  Gunnar Rätsch,et al.  Engineering Support Vector Machine Kerneis That Recognize Translation Initialion Sites , 2000, German Conference on Bioinformatics.

[32]  S. Salzberg,et al.  Improved microbial gene identification with GLIMMER. , 1999, Nucleic acids research.

[33]  Ethem Alpaydın,et al.  Combined 5 x 2 cv F Test for Comparing Supervised Classification Learning Algorithms , 1999, Neural Comput..

[34]  D. Goldberg,et al.  BOA: the Bayesian optimization algorithm , 1999 .

[35]  M. Kozak Initiation of translation in prokaryotes and eukaryotes. , 1999, Gene.

[36]  S. Salzberg,et al.  Interpolated Markov models for eukaryotic gene finding. , 1999, Genomics.

[37]  Joël Quinqueton,et al.  A Multi-agent System Simulating Human Splice Site Recognition , 1999, Comput. Chem..

[38]  Lloyd A. Smith,et al.  Feature Selection for Machine Learning: Comparing a Correlation-Based Filter Approach to the Wrapper , 1999, FLAIRS.

[39]  M. Van Montagu,et al.  Classification of Arabidopsis thaliana gene sequences: clustering of coding sequences into two groups according to codon usage improves gene prediction. , 1999, Journal of molecular biology.

[40]  M. Pelikán,et al.  The Bivariate Marginal Distribution Algorithm , 1999 .

[41]  Thorsten Joachims,et al.  Making large-scale support vector machine learning practical , 1999 .

[42]  Thomas G. Dietterich Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms , 1998, Neural Computation.

[43]  G. Rubin,et al.  A computer program for aligning a cDNA sequence with a genomic DNA sequence. , 1998, Genome research.

[44]  J. C. BurgesChristopher A Tutorial on Support Vector Machines for Pattern Recognition , 1998 .

[45]  M. Borodovsky,et al.  GeneMark.hmm: new solutions for gene finding. , 1998, Nucleic acids research.

[46]  S. Salzberg,et al.  Microbial gene identification using interpolated Markov models. , 1998, Nucleic acids research.

[47]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[48]  Pat Langley,et al.  Selection of Relevant Features and Examples in Machine Learning , 1997, Artif. Intell..

[49]  Heinz Mühlenbein,et al.  The Equation for Response to Selection and Its Use for Prediction , 1997, Evolutionary Computation.

[50]  Tom Fawcett,et al.  Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost Distributions , 1997, KDD.

[51]  S Brunak,et al.  A branch point consensus from Arabidopsis found by non-circular analysis allows for better prediction of acceptor sites. , 1997, Nucleic acids research.

[52]  Steven Salzberg,et al.  A method for identifying splice sites and translational start sites in eukaryotic mRNA , 1997, Comput. Appl. Biosci..

[53]  Victor V. Solovyev,et al.  The Gene-Finder Computer Tools for Analysis of Human and Model Organisms Genome Sequences , 1997, ISMB.

[54]  Anders Gorm Pedersen,et al.  Neural Network Prediction of Translation Initiation Sites in Eukaryotes: Perspectives for EST and Genome Analysis , 1997, ISMB.

[55]  S. Tiwari,et al.  Prediction of probable genes by Fourier analysis of genomic sequences , 1997, Comput. Appl. Biosci..

[56]  S. Karlin,et al.  Prediction of complete gene structures in human genomic DNA. , 1997, Journal of molecular biology.

[57]  Michael Ruogu Zhang,et al.  Identification of protein coding regions in the human genome by quadratic discriminant analysis. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[58]  David Haussler,et al.  Improved splice site detection in Genie , 1997, RECOMB '97.

[59]  Thomas G. Dietterich What is machine learning? , 2020, Archives of Disease in Childhood.

[60]  Peter G. Korning,et al.  Splice Site Prediction in Arabidopsis Thaliana Pre-mRNA by Combining Local and Global Sequence Information , 1996 .

[61]  Daphne Koller,et al.  Toward Optimal Feature Selection , 1996, ICML.

[62]  James W. Fickett,et al.  The Gene Identification Problem: An Overview for Developers , 1995, Comput. Chem..

[63]  Huan Liu,et al.  Chi2: feature selection and discretization of numeric attributes , 1995, Proceedings of 7th IEEE International Conference on Tools with Artificial Intelligence.

[64]  R Kole,et al.  Effects of secondary structure on pre‐mRNA splicing: hairpins sequestering the 5′ but not the 3′ splice site inhibit intron processing in Nicotiana plumbaginifolia. , 1995, The EMBO journal.

[65]  Victor V. Solovyev,et al.  Identification of Human Gene Structure Using Linear Discriminant Functions and Dynamic Programming , 1995, ISMB.

[66]  Ron Kohavi,et al.  Irrelevant Features and the Subset Selection Problem , 1994, ICML.

[67]  David B. Skalak,et al.  Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms , 1994, ICML.

[68]  Michael Ruogu Zhang,et al.  A sequence compilation and comparison of exons that are alternatively spliced in neurons. , 1994, Nucleic acids research.

[69]  Kenneth DeJong,et al.  Robust feature selection algorithms , 1993, Proceedings of 1993 IEEE Conference on Tools with Al (TAI-93).

[70]  Michael Q. Zhang,et al.  A weight array method for splicing signal analysis , 1993, Comput. Appl. Biosci..

[71]  Usama M. Fayyad,et al.  Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning , 1993, IJCAI.

[72]  Mark Borodovsky,et al.  GENMARK: Parallel Gene Recognition for Both DNA Strands , 1993, Comput. Chem..

[73]  J. Fickett,et al.  Assessment of protein coding measures. , 1992, Nucleic acids research.

[74]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[75]  Temple F. Smith,et al.  Prediction of gene structure. , 1992, Journal of molecular biology.

[76]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[77]  R. Voss,et al.  Evolution of long-range fractal correlations and 1/f noise in DNA base sequences. , 1992, Physical review letters.

[78]  U. Hobohm,et al.  Selection of representative protein data sets , 1992, Protein science : a publication of the Protein Society.

[79]  Anil K. Jain,et al.  Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[80]  T. D. Schneider,et al.  Sequence logos: a new way to display consensus sequences. , 1990, Nucleic acids research.

[81]  J. G. Patton,et al.  Scanning from an independently specified branch point defines the 3′ splice site of mammalian introns , 1989, Nature.

[82]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[83]  Jack Sklansky,et al.  On Automatic Feature Selection , 1988, Int. J. Pattern Recognit. Artif. Intell..

[84]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems , 1988 .

[85]  M. Kozak An analysis of 5'-noncoding sequences from 699 vertebrate messenger RNAs. , 1987, Nucleic acids research.

[86]  B. Ganem RNA world , 1987, Nature.

[87]  R. Linsker,et al.  A measure of DNA periodicity. , 1986, Journal of theoretical biology.

[88]  Max Henrion,et al.  Propagating uncertainty in bayesian networks by probabilistic logic sampling , 1986, UAI.

[89]  R. Staden,et al.  Computer methods to locate signals in nucleic acid sequences , 1984, Nucleic Acids Res..

[90]  Bibliography. , 1902, The British Journal for the History of Science.

[91]  Jan M. Van Campenhout,et al.  On the Possible Orderings in the Measurement Selection Problem , 1977, IEEE Transactions on Systems, Man, and Cybernetics.

[92]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[93]  Andrew J. Viterbi,et al.  Error bounds for convolutional codes and an asymptotically optimum decoding algorithm , 1967, IEEE Trans. Inf. Theory.

[94]  S. Sinha A Duality Theorem for Nonlinear Programming , 1966 .

[95]  Richard Bellman,et al.  Adaptive Control Processes: A Guided Tour , 1961, The Mathematical Gazette.

[96]  R. Amann,et al.  Predictive Identification of Exonic Splicing Enhancers in Human Genes , 2022 .