Machine learning in bioinformatics

Random Forests

Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works

Maximum Likelihood from Incomplete Data Via the <i>EM</i> Algorithm

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Genetic algorithms in search, optimization, and machine learning

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Optimization by Simulated Annealing

Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983) · Science · doi:10.1126/science.220.4598.671 · Cited by 45,134 · see citing works

The Nature of Statistical Learning Theory

Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138 · see citing works

Equation of State Calculations by Fast Computing Machines

Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953) · The Journal of Chemical Physics · doi:10.1063/1.1699114 · Cited by 37,192 · see citing works

Learning representations by back-propagating errors

Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892 · see citing works

The Elements of Statistical Learning

Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162 · see citing works

Untitled work

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A decision-theoretic generalization of on-line learning and an application to boosting

Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466 · see citing works

Pattern Classification

Seel, N. M. (2012) · Springer eBooks · doi:10.1007/978-1-4419-1428-6_5195 · Cited by 19,525 · see citing works

A logical calculus of the ideas immanent in nervous activity

McCulloch, W. S., & Pitts, W. (1943) · The Bulletin of Mathematical Biophysics · doi:10.1007/bf02478259 · Cited by 18,566 · see citing works

Genetic Algorithms in Search, Optimization and Machine Learning

Goldberg, D. E., & Robson, D. (1988) · Cited by 18,391 · see citing works

Bootstrap methods: another look at the jackknife

Efron, B. (1979) · The Annals of Statistics · doi:10.1214/aos/1176344552 · Cited by 17,602 · see citing works

Pattern classification and scene analysis

Grenander, U., Duda, R., & Hart, P. (1974) · A Wiley-Interscience publication · doi:10.2307/1573081 · Cited by 17,399 · see citing works

A Simple, Fast, and Accurate Algorithm to Estimate Large Phylogenies by Maximum Likelihood

Guindon, S., & Gascuel, O. (2003) · Systematic Biology · doi:10.1080/10635150390235520 · Cited by 16,698 · see citing works

Untitled work

(1988) · Cited by 16,582 · see citing works

Bagging predictors

Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325 · see citing works

THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS

Fisher, R. (1936) · Annals of Eugenics · doi:10.1111/j.1469-1809.1936.tb02137.x · Cited by 16,063 · see citing works

No free lunch theorems for optimization

Wolpert, D. H., & Macready, W. G. (1997) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.585893 · Cited by 14,237 · see citing works

Mfold web server for nucleic acid folding and hybridization prediction

Zuker, M. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkg595 · Cited by 13,404 · see citing works

Genetic Programming: On the Programming of Computers by Means of Natural Selection

Koza, J. R. (1992) · Medical Entomology and Zoology · Cited by 13,225 · see citing works

Pattern classification and scene analysis

Duda, R. O., & Hart, P. E. (1973) · CERN Document Server (European Organization for Nuclear Research) · Cited by 12,895 · see citing works

Introduction to Statistical Pattern Recognition

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Cross validatory choice and assessment of statistical predictions

Stone, M. (1974) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1974.tb00994.x · Cited by 10,881 · see citing works

Untitled work

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Advances in Neural Information Processing Systems 14

(2002) · The MIT Press eBooks · doi:10.7551/mitpress/1120.001.0001 · Cited by 9,184 · see citing works

Wrappers for feature subset selection

Kohavi, R., & John, G. H. (1997) · Artificial Intelligence · doi:10.1016/s0004-3702(97)00043-x · Cited by 8,940 · see citing works

Signal detection theory and psychophysics

Ingleby, D. (1967) · Journal of Sound and Vibration · doi:10.1016/0022-460x(67)90197-6 · Cited by 8,616 · see citing works

Stacked generalization

Wolpert, D. H. (1992) · Neural Networks · doi:10.1016/s0893-6080(05)80023-1 · Cited by 7,951 · see citing works

The use of the area under the ROC curve in the evaluation of machine learning algorithms

Bradley, A. P. (1997) · Pattern Recognition · doi:10.1016/s0031-3203(96)00142-2 · Cited by 7,217 · see citing works

An Algorithm for Vector Quantizer Design

Linde, Y., Buzo, A., & Gray, R. (1980) · IEEE Transactions on Communications · doi:10.1109/tcom.1980.1094577 · Cited by 7,201 · see citing works

Vector Quantization and Signal Compression

Gersho, A., & Gray, R. M. (1992) · Springer eBooks · doi:10.1007/978-1-4615-3626-0 · Cited by 7,055 · see citing works

Advances in kernel methods: support vector learning

Schölkopf, B., Burges, C. J. C., & Smola, A. J. (1999) · doi:10.5555/299094 · Cited by 5,801 · see citing works

CAUSALITY: MODELS, REASONING AND INFERENCE

Hitchcock, C., & Pearl, J. (2001) · The Philosophical Review · doi:10.2307/3182612 · Cited by 4,824 · see citing works

Note on the Sampling Error of the Difference Between Correlated Proportions or Percentages

McNemar, Q. (1947) · Psychometrika · doi:10.1007/bf02295996 · Cited by 4,754 · see citing works

Bayesian network classifiers

Friedman, N., Geiger, D., & Goldszmidt, M. (1997) · Machine Learning · doi:10.1023/a:1007465528199 · Cited by 4,742 · see citing works

Comprehensive Identification of Cell Cycle–regulated Genes of the YeastSaccharomyces cerevisiaeby Microarray Hybridization

Spellman, P. T., Sherlock, G. J., Zhang, M. Q., Iyer, V. R., Anders, K. R., Eisen, M. B., Brown, P. O., Botstein, D., & Futcher, B. (1998) · Molecular Biology of the Cell · doi:10.1091/mbc.9.12.3273 · Cited by 4,724 · see citing works

Future paths for integer programming and links to artificial intelligence

Glover, F. (1986) · Computers & Operations Research · doi:10.1016/0305-0548(86)90048-1 · Cited by 4,198 · see citing works

Bayesian Networks and Decision Graphs

Jensen, F. V., & Nielsen, T. D. (2007) · Information science and statistics · doi:10.1007/978-0-387-68282-2 · Cited by 4,157 · see citing works

Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms

Dietterich, T. G. (1998) · Neural Computation · doi:10.1162/089976698300017197 · Cited by 3,840 · see citing works

Pattern Classification

Abe, S. (2001) · Springer eBooks · doi:10.1007/978-1-4471-0285-4 · Cited by 3,740 · see citing works

Biological sequence analysis probabilistic models of proteins and nucleic acids

Durbin, R. M., Eddy, S. R., Krogh, A., & Mitchison, G. (1998) · Cited by 3,565 · see citing works

A Probabilistic Theory of Pattern Recognition

Devroye, L., Györfi, L., & Lugosi, G. (1996) · Stochastic Modelling and Applied Probability · doi:10.1007/978-1-4612-0711-5 · Cited by 3,274 · see citing works

Biological Sequence Analysis

Durbin, R. M., Eddy, S. R., Krogh, A., & Mitchison, G. (1998) · Cambridge University Press eBooks · doi:10.1017/cbo9780511790492 · Cited by 3,255 · see citing works

Using Bayesian Networks to Analyze Expression Data

Friedman, N., Michal Linial, B., Nachman, I., & Pe’er, D. (2000) · Journal of Computational Biology · doi:10.1089/106652700750050961 · Cited by 3,248 · see citing works

Combining Pattern Classifiers: Methods and Algorithms

Bagui, S. (2005) · Technometrics · doi:10.1198/tech.2005.s320 · Cited by 3,224 · see citing works

Floating search methods in feature selection

Pudil, P., Novovičová, J., & Kittler, J. (1994) · Pattern Recognition Letters · doi:10.1016/0167-8655(94)90127-9 · Cited by 3,129 · see citing works

Combining Pattern Classifiers

Kuncheva, L. I. (2004) · Wiley eBooks · doi:10.1002/0471660264 · Cited by 3,007 · see citing works

Graphical Models

Lauritzen, S. L. (1996) · doi:10.1093/oso/9780198522195.001.0001 · Cited by 2,976 · see citing works

Causality: models, reasoning, and inference

(2000) · Choice Reviews Online · doi:10.5860/choice.38-1494a · Cited by 2,969 · see citing works

Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation

Tamayo, P., Slonim, D. K., Mesirov, J. P., Zhu, Q., Kitareewan, S., Dmitrovsky, E., Lander, E. S., & Golub, T. R. (1999) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.96.6.2907 · Cited by 2,924 · see citing works

Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data

Dudoit, S., Fridlyand, J., & Speed, T. P. (2002) · Journal of the American Statistical Association · doi:10.1198/016214502753479248 · Cited by 2,673 · see citing works

Discriminant Analysis and Statistical Pattern Recognition.

Hawkins, D. M., & McLachlan, G. J. (1993) · Journal of the American Statistical Association · doi:10.2307/2290355 · Cited by 2,662 · see citing works

Approximating discrete probability distributions with dependence trees

Chow, C., & Liu, C. (1968) · IEEE Transactions on Information Theory · doi:10.1109/tit.1968.1054142 · Cited by 2,638 · see citing works

Discriminatory Analysis. Nonparametric Discrimination: Consistency Properties

Fix, E., & Hodges, J. L. (1989) · International Statistical Review · doi:10.2307/1403797 · Cited by 2,563 · see citing works

Knowledge-based analysis of microarray gene expression data by using support vector machines

Brown, M. P. S., Noble, W. S., Lin, D. M., Cristianini, N., Sugnet, C. W., Furey, T. S., Ares, M., & Haussler, D. H. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.97.1.262 · Cited by 2,310 · see citing works

Estimating the error rate of a prediction rule: improvement on cross-validation

Efron, B. (1983) · Journal of the American Statistical Association · doi:10.1080/01621459.1983.10477973 · Cited by 2,300 · see citing works

The Nature of Statistical Learning

Vapnik, V. N. (1995) · Medical Entomology and Zoology · Cited by 2,295 · see citing works

Feature Selection for Knowledge Discovery and Data Mining

Liu, H., & Motoda, H. (1998) · Springer eBooks · doi:10.1007/978-1-4615-5689-3 · Cited by 2,216 · see citing works

Cluster analysis of multivariate data : efficiency versus interpretability of classifications

Forgy, E. W. (1965) · Biometrics · Cited by 2,124 · see citing works

Modeling of loops in protein structures

Fiser, A., Do, R. K. G., & Šali, A. (2000) · Protein Science · doi:10.1110/ps.9.9.1753 · Cited by 2,116 · see citing works

Assessing the accuracy of prediction algorithms for classification: an overview

Baldi, P., Brunak, S., Chauvin, Y., Andersen, C. A. F., & Nielsen, H. (2000) · Bioinformatics · doi:10.1093/bioinformatics/16.5.412 · Cited by 2,111 · see citing works

The computational complexity of probabilistic inference using bayesian belief networks

Cooper, G. F. (1990) · Artificial Intelligence · doi:10.1016/0004-3702(90)90060-d · Cited by 1,962 · see citing works

Hidden Markov Models in Computational Biology

Krogh, A., Brown, M., Mian, S. I., Sjölander, K. V., & Haussler, D. H. (1994) · Journal of Molecular Biology · doi:10.1006/jmbi.1994.1104 · Cited by 1,948 · see citing works

Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation

Larraanaga, P., & Lozano, J. A. (2001) · Kluwer Academic Publishers eBooks · Cited by 1,908 · see citing works

Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms

White, B., & Rosenblatt, F. (1963) · The American Journal of Psychology · doi:10.2307/1419730 · Cited by 1,818 · see citing works

Statistical Pattern Recognition

Martinez, W. L., & Martinez, A. R. (2001) · Chapman & Hall/CRC eBooks · doi:10.1201/9781420035636-10 · Cited by 1,748 · see citing works

Bayesian Networks and Decision Graphs

Jensen, F. V. (2001) · Springer eBooks · doi:10.1007/978-1-4757-3502-4 · Cited by 1,743 · see citing works

Probabilistic Networks and Expert Systems

Cowell, R. G., Spiegelhalter, D. J., Lauritzen, S. L., & Dawid, A. P. (1999) · Springer eBooks · doi:10.1007/b97670 · Cited by 1,740 · see citing works

Discriminatory analysis: Nonparametric discrimination: Consistency properties

Fix, E., & Hodges, J. L. (1951) · PsycEXTRA Dataset · doi:10.1037/e471672008-001 · Cited by 1,731 · see citing works

Steps toward Artificial Intelligence

Minsky, M. (1961) · Proceedings of the IRE · doi:10.1109/jrproc.1961.287775 · Cited by 1,706 · see citing works

Introduction to Metropolis, Rosenbluth, Rosenbluth, Teller, and Teller (1953) Equations of State Calculations by Fast Computing Machines. J. Chem. Phys.,21, 1087–1092. and Geman and Geman (1984) Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images. IEEE Trans. Pattern Anal. Machine Intelligence,6, 721–741.

Huber, P. J. (1997) · Springer series in statistics · doi:10.1007/978-1-4612-0667-5_6 · Cited by 1,556 · see citing works

Conditional Independence in Statistical Theory

Dawid, A. P. (1979) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1979.tb01052.x · Cited by 1,507 · see citing works

Selection bias in gene extraction on the basis of microarray gene-expression data

Ambroise, C., & McLachlan, G. J. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.102102699 · Cited by 1,487 · see citing works

Statistical Pattern Recognition

Webb, A. R. (2002) · Wiley eBooks · doi:10.1002/0470854774 · Cited by 1,468 · see citing works

Learning Bayesian Networks

Sammut, C., & Webb, G. I. (2017) · Encyclopedia of Machine Learning and Data Mining · doi:10.1007/978-1-4899-7687-1_100245 · Cited by 1,438 · see citing works

Estimation of Distribution Algorithms

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Discriminant Analysis and Statistical Pattern Recognition

McLachlan, G. J. (1992) · Wiley series in probability and statistics · doi:10.1002/0471725293 · Cited by 1,272 · see citing works

A Branch and Bound Algorithm for Feature Subset Selection

Narendra, & Fukunaga (1977) · IEEE Transactions on Computers · doi:10.1109/tc.1977.1674939 · Cited by 1,266 · see citing works

Inferring Cellular Networks Using Probabilistic Graphical Models

Friedman, N. (2004) · Science · doi:10.1126/science.1094068 · Cited by 1,208 · see citing works

Inference for the Generalization Error

Nadeau, C., & Bengio, Y. (2003) · Machine Learning · doi:10.1023/a:1024068626366 · Cited by 1,092 · see citing works

Kernel Methods in Computational Biology

Vert, J., & 阿久津, 達. (2004) · The MIT Press eBooks · doi:10.7551/mitpress/4057.001.0001 · Cited by 1,051 · see citing works

Prediction of cancer outcome with microarrays: a multiple random validation strategy

Michiels, S., Koscielny, S., & Hill, C. (2005) · The Lancet · doi:10.1016/s0140-6736(05)17866-0 · Cited by 1,029 · see citing works

Bioinformatics the machine learning approach

Baldi, P., & Brunak, S. (n.d.) · Cited by 925 · see citing works

A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis

Statnikov, A., Aliferis, C., Tsamardinos, I., Hardin, D. P., & Levy, S. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bti033 · Cited by 837 · see citing works

An empirical Bayes approach to inferring large-scale gene association networks

Schäfer, J., & Strimmer, K. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bti062 · Cited by 786 · see citing works

Tissue Classification with Gene Expression Profiles

Ben‐Dor, A., Bruhn, L., Friedman, N., Nachman, I., Schummer, M., & Zohar Yakhini, B. (2000) · Journal of Computational Biology · doi:10.1089/106652700750050943 · Cited by 742 · see citing works

Validating clustering for gene expression data

Yeung, K. Y., Haynor, D. R., & Ruzzo, W. L. (2001) · Bioinformatics · doi:10.1093/bioinformatics/17.4.309 · Cited by 713 · see citing works

Mathematical Taxonomy

Jardine, N., & Sibson, R. (1974) · Journal of Marketing Research · doi:10.2307/3151168 · Cited by 639 · see citing works

Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method

Li, L., Weinberg, C. R., Darden, T. A., & Pedersen, L. G. (2001) · Bioinformatics · doi:10.1093/bioinformatics/17.12.1131 · Cited by 638 · see citing works

Feature selection for high-dimensional genomic microarray data

Xing, E. P., Jordan, M. I., & Karp, R. M. (2001) · Cited by 635 · see citing works

A hierarchical unsupervised growing neural network for clustering gene expression patterns

Herrero, J., Valencia, A., & Dopazo, J. (2001) · Bioinformatics · doi:10.1093/bioinformatics/17.2.126 · Cited by 622 · see citing works

Computational Molecular Biology

Pevzner, P. A. (2000) · The MIT Press eBooks · doi:10.7551/mitpress/2022.001.0001 · Cited by 613 · see citing works

Is cross-validation valid for small-sample microarray classification?

Braga-Neto, U., & Dougherty, E. R. (2004) · Bioinformatics · doi:10.1093/bioinformatics/btg419 · Cited by 599 · see citing works

Inferring subnetworks from perturbed expression profiles

Pe’er, D., Regev, A., Elidan, G., & Friedman, N. (2001) · Bioinformatics · doi:10.1093/bioinformatics/17.suppl_1.s215 · Cited by 548 · see citing works

Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks

Husmeier, D. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg313 · Cited by 535 · see citing works

A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments

Pan, W. (2002) · Bioinformatics · doi:10.1093/bioinformatics/18.4.546 · Cited by 522 · see citing works

Principles of Neurodynamics. Perceptrons and the Theory of Brain Mechanisms.

Orbach, J. (1962) · Archives of General Psychiatry · doi:10.1001/archpsyc.1962.01720030064010 · Cited by 514 · see citing works

Current methods of gene prediction, their strengths and weaknesses

Mathé, C. (2002) · Nucleic Acids Research · doi:10.1093/nar/gkf543 · Cited by 478 · see citing works

Bioinformatics: The Machine Learning Approach

Leunissen, J. A. M. (2002) · Briefings in Bioinformatics · doi:10.1093/bib/3.3.321 · Cited by 477 · see citing works

Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data

Wu, B., Abbott, T., Fishman, D. A., McMurray, W. J., Mor, G., Stone, K. L., Ward, D. C., Williams, K. R., & Zhao, H. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg210 · Cited by 470 · see citing works

Computational Modeling of Genetic and Biochemical Networks

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Learning Gaussian Networks

Geiger, D., & Heckerman, D. E. (1994) · Elsevier eBooks · doi:10.1016/b978-1-55860-332-5.50035-3 · Cited by 435 · see citing works

Combined 5 × 2 cv F Test for Comparing Supervised Classification Learning Algorithms

Alpaydın, E. (1999) · Neural Computation · doi:10.1162/089976699300016007 · Cited by 432 · see citing works

Filter versus wrapper gene selection approaches in DNA microarray domains

Inza, I., Larrañaga, P., Blanco, R. E., & Cerrolaza, A. J. (2004) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2004.01.007 · Cited by 426 · see citing works

Gene selection from microarray data for cancer classification—a machine learning approach

Wang, Y., Tetko, I. V., Hall, M. A., et al. (2005) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2004.11.001 · Cited by 412 · see citing works

Learning on Bayesian Networks

Vagis, A. A. (2002) · Journal of Automation and Information Sciences · doi:10.1615/jautomatinfscien.v34.i6.40 · Cited by 405 · see citing works

Dynamic modeling of genetic networks using genetic algorithm and S-system

Kikuchi, S., Tominaga, D., Arita, M., Takahashi, K., & Tomita, M. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg027 · Cited by 401 · see citing works

Ensemble machine learning on gene expression data for cancer classification.

Tan, A. C., & Gilbert, D. (2003) · PubMed · Cited by 400 · see citing works

An extensive comparison of recent classification tools applied to microarray data

Lee, J. W., Lee, J. B., Park, M., & Song, S. H. (2004) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2004.03.017 · Cited by 394 · see citing works

USING GRAPHICAL MODELS AND GENOMIC EXPRESSION DATA TO STATISTICALLY VALIDATE MODELS OF GENETIC REGULATORY NETWORKS

Hartemink, A. J., Gifford, D. K., Jaakkola, T., & Young, R. A. (2000) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789814447362_0042 · Cited by 392 · see citing works

Promoter2.0: for the recognition of PolII promoter sequences.

Knudsen, S. (1999) · Bioinformatics · doi:10.1093/bioinformatics/15.5.356 · Cited by 391 · see citing works

Text Mining for Biology And Biomedicine

Ananiadou, S., & McNaught, J. (2005) · Cited by 367 · see citing works

Learning limited dependence Bayesian classifiers

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A new approach to the rapid determination of protein side chain conformations.

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Modelling Gene Expression Data using Dynamic Bayesian Networks

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Parallel Problem Solving from Nature — PPSN VII

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Gaussian Influence Diagrams

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Nonparametric methods for identifying differentially expressed genes in microarray data

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Prediction of Protein Secondary Structure by Combining Nearest-neighbor Algorithms and Multiple Sequence Alignments

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Molecular classification of multiple tumor types

Yeang, C., Ramaswamy, S., Tamayo, P., et al. (2001) · Bioinformatics · doi:10.1093/bioinformatics/17.suppl_1.s316 · Cited by 274 · see citing works

Learning Bayesian network structures by searching for the best ordering with genetic algorithms

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Genome-wide identification of genes likely to be involved in human genetic disease

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Inference of S-system models of genetic networks using a cooperative coevolutionary algorithm

Kimura, S., Ide, K., Kashihara, A., Kano, M., Okada, M., Masui, R., Nakagawa, N., Yokoyama, S., Kuramitsu, S., & Konagaya, A. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bti071 · Cited by 259 · see citing works

Genetic algorithms applied to multi-class prediction for the analysis of gene expression data

Ooi, C.-H., & Tan, P. (2002) · Bioinformatics · doi:10.1093/bioinformatics/19.1.37 · Cited by 258 · see citing works

Rich probabilistic models for gene expression

Segal, E., Taskar, B., Gasch, A. P., Friedman, N., & Koller, D. (2001) · Bioinformatics · doi:10.1093/bioinformatics/17.suppl_1.s243 · Cited by 256 · see citing works

A Dynamic Programming Approach to De Novo Peptide Sequencing via Tandem Mass Spectrometry

Chen, T., Kao, M., Tepel, M., Rush, A. J., & Church, G. M. (2001) · Journal of Computational Biology · doi:10.1089/10665270152530872 · Cited by 249 · see citing works

Prediction of protein subcellular locations using fuzzy k-NN method

Huang, Y., & Li, Y. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg366 · Cited by 238 · see citing works

Feature Subset Selection by Bayesian network-based optimization

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