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Feature selection methods for big data bioinformatics: A survey from the search perspective
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Kennedy, J., & Eberhart, R. (2002) · Proceedings of ICNN'95 - International Conference on Neural Networks · doi:10.1109/icnn.1995.488968 · Cited by 58,192
Holland, J. H. (1992) · The MIT Press eBooks · doi:10.7551/mitpress/1090.001.0001 · Cited by 47,002
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
Regularization and Variable Selection Via the Elastic Net
Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957
Pawlak, Z. (1982) · International Journal of Computer & Information Sciences · doi:10.1007/bf01001956 · Cited by 12,322
Golub, T. R., Slonim, D. K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J. P., Coller, H., Loh, M. L., Downing, J. R., Caligiuri, M. A., et al. (1999) · Science · doi:10.1126/science.286.5439.531 · Cited by 11,516
In silico prediction of protein-protein interactions in human macrophages
Souiai, O., Guerfali, F., Ben Miled, S., Brun, C., & Benkahla, A. (2014) · BMC Research Notes · doi:10.1186/1756-0500-7-157 · Cited by 10,803
Significance analysis of microarrays applied to the ionizing radiation response
Tusher, V. G., Tibshirani, R., & Chu, G. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.091062498 · Cited by 10,568
Gene selection for cancer classification using support vector machines
Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002) · Machine Learning · doi:10.1023/a:1012487302797 · Cited by 9,954
Gene expression profiling predicts clinical outcome of breast cancer
van 't Veer, L. J., Dai, H., van de Vijver, M. J., He, Y. D., Hart, A. A. M., Mao, M., Peterse, H. L., van der Kooy, K., Marton, M. J., Witteveen, A. T., et al. (2002) · Nature · doi:10.1038/415530a · Cited by 9,536
A review of feature selection techniques in bioinformatics
Saeys, Y., Inza, I., & Larrañaga, P. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm344 · Cited by 5,244
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 4,181
Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks
Broomhead, D. S., & Lowe, D. (1988) · OpenGrey (Institut de l'Information Scientifique et Technique) · Cited by 3,443
Machine Learning Applications in Cancer Prognosis and Prediction
Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., & Fotiadis, D. I. (2015) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2014.11.005 · Cited by 3,338
Optimization, Learning and Natural Algorithms
Dorigo, M. (1992) · Medical Entomology and Zoology · Cited by 3,307
Estimating attributes: Analysis and extensions of RELIEF
Kononenko, I. (1994) · Lecture Notes in Computer Science · doi:10.1007/3-540-57868-4_57 · Cited by 2,958
MINIMUM REDUNDANCY FEATURE SELECTION FROM MICROARRAY GENE EXPRESSION DATA
DING, C., & PENG, H. (2005) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s0219720005001004 · Cited by 2,751
Fisher discriminant analysis with kernels
Mika, S., Ratsch, G., Weston, J., Scholkopf, B., & Mullers, K. R. (2003) · Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468) · doi:10.1109/nnsp.1999.788121 · Cited by 2,661
Ridge Regression and Other Kernels for Genomic Selection with R Package rrBLUP
Endelman, J. B. (2011) · The Plant Genome · doi:10.3835/plantgenome2011.08.0024 · Cited by 2,094
An Automatic Method for Solving Discrete Programming Problems
Land, A. H., & Doig, A. G. (2010) · 50 Years of Integer Programming 1958-2008 · doi:10.1007/978-3-540-68279-0_5 · Cited by 1,988
An Automatic Method of Solving Discrete Programming Problems
Land, A. H., & Doig, A. G. (1960) · Econometrica · doi:10.2307/1910129 · Cited by 1,942
The feature selection problem: traditional methods and a new algorithm
Kira, K., & Rendell, L. A. (1992) · National Conference on Artificial Intelligence · Cited by 1,897
Toward optimal feature selection
Koller, D., & Sahami, M. (1996) · Cited by 1,482
Big Data: Astronomical or Genomical?
Stephens, Z. D., Lee, S. Y., Faghri, F., Campbell, R. H., Zhai, C., Efron, M. J., Iyer, R., Schatz, M. C., Sinha, S., & Robinson, G. E. (2015) · PLOS Biology · doi:10.1371/journal.pbio.1002195 · Cited by 1,340
Analysis of Variance for Gene Expression Microarray Data
Kerr, M. K., Martin, M., & Churchill, G. A. (2000) · Journal of Computational Biology · doi:10.1089/10665270050514954 · Cited by 1,310
Learning and generalization characteristics of the random vector functional-link net
Pao, Y.-H., Park, G.-H., & Sobajic, D. J. (1994) · Neurocomputing · doi:10.1016/0925-2312(94)90053-1 · Cited by 1,138
A Direct Method of Nonparametric Measurement Selection
Whitney, A. W. (1971) · IEEE Transactions on Computers · doi:10.1109/t-c.1971.223410 · Cited by 851
Dimensionality reduction using genetic algorithms
Raymer, M. L., Punch, W. F., Goodman, E. D., Kuhn, L. A., & Jain, A. K. (2000) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.850656 · Cited by 840
The mutual information: detecting and evaluating dependencies between variables
Steuer, R., Kurths, J., Daub, C. O., Weise, J., & Selbig, J. (2002) · Bioinformatics · doi:10.1093/bioinformatics/18.suppl_2.s231 · Cited by 815
Ridge Regression Learning Algorithm in Dual Variables
Saunders, C. J., Gammerman, A., & Vovk, V. (1998) · ePrints Soton (University of Southampton) · Cited by 811
A note on genetic algorithms for large-scale feature selection
Siedlecki, W., & Sklansky, J. (1989) · Pattern Recognition Letters · doi:10.1016/0167-8655(89)90037-8 · Cited by 806
A NOTE ON GENETIC ALGORITHMS FOR LARGE-SCALE FEATURE SELECTION
SIEDLECKI, W., & SKLANSKY, J. (1993) · Handbook of Pattern Recognition and Computer Vision · doi:10.1142/9789814343138_0005 · Cited by 765
On the effectiveness of receptors in recognition systems
Marill, T., & Green, D. (1963) · IEEE Transactions on Information Theory · doi:10.1109/tit.1963.1057810 · Cited by 664
Chaotic simulated annealing by a neural network model with transient chaos
Chen, L., & Aihara, K. (1995) · Neural Networks · doi:10.1016/0893-6080(95)00033-v · Cited by 661
Feature selection using Joint Mutual Information Maximisation
Bennasar, M., Hicks, Y., & Setchi, R. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.07.007 · Cited by 659
Induction of Selective Bayesian Classifiers
LANGLEY, P., & SAGE, S. (1994) · Uncertainty Proceedings 1994 · doi:10.1016/b978-1-55860-332-5.50055-9 · Cited by 657
Li, L., Weinberg, C. R., Darden, T. A., & Pedersen, L. G. (2001) · Bioinformatics · doi:10.1093/bioinformatics/17.12.1131 · Cited by 638
A survey on filter techniques for feature selection in gene expression microarray analysis
Lazar, C., Taminau, J., Meganck, S., Steenhoff, D., Coletta, A., Molter, C., de Schaetzen, V., Duque, R., Bersini, H., & Nowe, A. (2012) · IEEE/ACM Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2012.33 · Cited by 619
Langley, P. (1995) · Medical Entomology and Zoology · Cited by 512
Multiple SVM-RFE for Gene Selection in Cancer Classification With Expression Data
Duan, K.-B., Rajapakse, J. C., Wang, H., & Azuaje, F. (2005) · IEEE Transactions on Nanobioscience · doi:10.1109/tnb.2005.853657 · Cited by 468
Data Mining with Computational Intelligence
(2005) · Advanced Information and Knowledge Processing · doi:10.1007/3-540-28803-1 · Cited by 409
Combating the Small Sample Class Imbalance Problem Using Feature Selection
Wasikowski, M., & Chen, X.-W. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.187 · Cited by 390
Thomas, J. G., Olson, J. M., Tapscott, S. J., & Zhao, L. P. (2001) · Genome Research · doi:10.1101/gr.165101 · Cited by 316
Fu, X., & Wang, L. (2003) · IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) · doi:10.1109/tsmcb.2003.810911 · Cited by 312
Self-citationAn, S., Liu, W., & Venkatesh, S. (2007) · Pattern Recognition · doi:10.1016/j.patcog.2006.12.015 · Cited by 301
Accurate Cancer Classification Using Expressions of Very Few Genes
Wang, L., Chu, F., & Xie, W. (2007) · IEEE/ACM Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2007.1006 · Cited by 290
Self-citationA neural network model as a globally coupled map and applications based on chaos
Nozawa, H. (1992) · Chaos: An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.165880 · Cited by 280
Cost-Sensitive Learning with Neural Networks.
Kukar, M., & Kononenko, I. (1998) · Cited by 273
Combined SVM-Based Feature Selection and Classification
Neumann, J., Schnörr, C., & Steidl, G. (2005) · Machine Learning · doi:10.1007/s10994-005-1505-9 · Cited by 248
Mohabatkar, H., Mohammad Beigi, M., & Esmaeili, A. (2011) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2011.04.017 · Cited by 214
Development of Two-Stage SVM-RFE Gene Selection Strategy for Microarray Expression Data Analysis
Tang, Y., Zhang, Y.-Q., & Huang, Z. (2007) · IEEE/ACM Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2007.1028 · Cited by 206
Hybrid huberized support vector machines for microarray classification and gene selection
Wang, L., Zhu, J., & Zou, H. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btm579 · Cited by 189
Self-citationAn Efficient Semi-Unsupervised Gene Selection Method via Spectral Biclustering
Liu, B., Wan, C., & Wang, L. (2006) · IEEE Transactions on Nanobioscience · doi:10.1109/tnb.2006.875040 · Cited by 188
Self-citationOn chaotic simulated annealing
Wang, L., & Smith, K. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/72.701185 · Cited by 186
Self-citationAPPLICATIONS OF SUPPORT VECTOR MACHINES TO CANCER CLASSIFICATION WITH MICROARRAY DATA
CHU, F., & WANG, L. (2005) · International Journal of Neural Systems · doi:10.1142/s0129065705000396 · Cited by 183
Self-citationAn Entropy-based gene selection method for cancer classification using microarray data
Liu, X., Krishnan, A., & Mondry, A. (2005) · BMC Bioinformatics · doi:10.1186/1471-2105-6-76 · Cited by 183
Feature selection and nearest centroid classification for protein mass spectrometry
Levner, I. (2005) · BMC Bioinformatics · doi:10.1186/1471-2105-6-68 · Cited by 172
Wang, L., Li, S., Tian, F., & Fu, X. (2004) · IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) · doi:10.1109/tsmcb.2004.829778 · Cited by 169
Self-citationFeature Selection via Global Redundancy Minimization
Wang, D., Nie, F., & Huang, H. (2015) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2015.2426703 · Cited by 163
Feature selection from huge feature sets
Bins, J., & Draper, B. A. (2002) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2001.937619 · Cited by 140
Mohapatra, P., Chakravarty, S., & Dash, P. K. (2016) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2016.02.002 · Cited by 139
Li, X., & Yin, M. (2013) · IEEE Transactions on NanoBioscience · doi:10.1109/tnb.2013.2294716 · Cited by 133
Naik, G. R., & Nguyen, H. T. (2014) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2014.2326660 · Cited by 130
Yousef, M., Jung, S. C., Showe, L. C., & Showe, M. K. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-144 · Cited by 128
A General Wrapper Approach to Selection of Class-Dependent Features
Wang, L., Zhou, N., & Chu, F. (2008) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2008.2000395 · Cited by 126
Self-citationSemisupervised Biased Maximum Margin Analysis for Interactive Image Retrieval
Zhang, L., Wang, L., & Lin, W. (2011) · IEEE Transactions on Image Processing · doi:10.1109/tip.2011.2177846 · Cited by 119
Self-citationA Modified T-Test Feature Selection Method and Its Application on the HapMap Genotype Data
Zhou, N., & Wang, L. (2007) · Genomics Proteomics & Bioinformatics · doi:10.1016/s1672-0229(08)60011-x · Cited by 114
Self-citationChen, C. C. M., Schwender, H. R., Keith, J. M., Nunkesser, R., Mengersen, K. L., & Macrossan, P. E. (2011) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2011.46 · Cited by 107
Stable Gene Selection from Microarray Data via Sample Weighting
Yu, L., Han, Y., & Berens, M. E. (2011) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2011.47 · Cited by 98
Verma, B., & Rahman, A. (2011) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2011.28 · Cited by 93
Efficient quadratic regularization for expression arrays
Hastie, T. J. (2004) · Biostatistics · doi:10.1093/biostatistics/5.3.329 · Cited by 93
Cancer Classification from Gene Expression Data by NPPC Ensemble
Ghorai, S., Mukherjee, A., Sengupta, S., & Dutta, P. K. (2010) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2010.36 · Cited by 89
Feature Selection for Gene Expression Using Model-Based Entropy
Shenghuo Zhu, B., Wang, D., Yu, K. F., Li, T., & Gong, Y. (2009) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2008.35 · Cited by 85
Feature Selection and Combination Criteria for Improving Accuracy in Protein Structure Prediction
Lin, K.-L., Lin, C., Huang, C.-D., Chang, H.-M., Yang, C.-Y., Lin, C., Tang, C. Y., & Hsu, D. F. (2007) · IEEE Transactions on NanoBioscience · doi:10.1109/tnb.2007.897482 · Cited by 85
Efficient quadratic regularization for expression arrays
Hastie, T. J. (2004) · Biostatistics · doi:10.1093/biostatistics/kxh010 · Cited by 81
Zhu, M. F., & Wang, L. (2010) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2010.5596301 · Cited by 75
Self-citationConjunctive Patches Subspace Learning With Side Information for Collaborative Image Retrieval
Zhang, L., Wang, L., & Lin, W. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2195014 · Cited by 66
Self-citationThe PDF projection theorem and the class-specific method
Baggenstoss, P. M. (2003) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2002.808109 · Cited by 65
Gene Selection Using Iterative Feature Elimination Random Forests for Survival Outcomes
Pang, H., George, S. L., Hui, K. Y., & Tong, T. (2012) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2012.63 · Cited by 65
Laplacian Linear Discriminant Analysis Approach to Unsupervised Feature Selection
Niijima, S., & Okuno, Y. (2009) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2007.70257 · Cited by 63
Fuzzy Preference Based Feature Selection and Semisupervised SVM for Cancer Classification
Maulik, U., & Chakraborty, D. (2014) · IEEE Transactions on NanoBioscience · doi:10.1109/tnb.2014.2312132 · Cited by 62
Identification of Full and Partial Class Relevant Genes
Zhu, Z., Ong, Y., & Żurada, J. (2010) · IEEE/ACM Transactions on Computational Biology & Bioinformatics · doi:10.1109/tcbb.2008.105 · Cited by 62
Genetic Networks and Soft Computing
Mitra, S., Das, R., & Hayashi, Y. (2009) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2009.39 · Cited by 60
Feature-Selected Tree-Based Classification
Freeman, C., Kulić, D., & Basir, O. (2013) · IEEE Transactions on Cybernetics · doi:10.1109/tsmcb.2012.2237394 · Cited by 59
Geometric Optimum Experimental Design for Collaborative Image Retrieval
Zhang, L., Wang, L., Lin, W., et al. (2013) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2013.2276172 · Cited by 58
Self-citationSNP Selection and Classification of Genome-Wide SNP Data Using Stratified Sampling Random Forests
Wu, Q., Ye, Y., Liu, Y., & Ng, M. K. (2012) · IEEE Transactions on NanoBioscience · doi:10.1109/tnb.2012.2214232 · Cited by 57
An adaptive learning algorithm for principal component analysis
Chen, L.-H., & Chang, S. (1995) · IEEE Transactions on Neural Networks · doi:10.1109/72.410369 · Cited by 57
Braga-Neto, U. (2007) · IEEE Signal Processing Magazine · doi:10.1109/msp.2007.273062 · Cited by 57
Lopes, F. M., Martins, D., Barrera, J., & Junior, R. M. C. (2014) · Information Sciences · doi:10.1016/j.ins.2014.02.096 · Cited by 55
A scalable approach to simultaneous evolutionary instance and feature selection
García‐Pedrajas, N., de Haro-García, A., & Pérez-Rodríguez, J. (2012) · Information Sciences · doi:10.1016/j.ins.2012.10.006 · Cited by 55
Data Mining on DNA Sequences of Hepatitis B Virus
Leung, K., Lee, K.-H., Wang, J., Ng, E. Y. T., Chan, H. L., Tsui, S. K., Mok, T. S. K., Tse, P. C., & Sung, J. J. �. (2009) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2009.6 · Cited by 55
Wu, M., Dai, D., Shi, Y., Yan, H., & Zhang, X.-F. (2012) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2012.105 · Cited by 51
Li, H., Liang, Y., Xu, Q., Cao, D., Tan, B., Deng, B., & Lin, C. (2011) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2011.36 · Cited by 51
A Weighted Principal Component Analysis and Its Application to Gene Expression Data
da Costa, J. P., Alonso, H., & Roque, L. (2009) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2009.61 · Cited by 49
Liu, C., & Sako, H. (2005) · Pattern Recognition · doi:10.1016/j.patcog.2005.04.021 · Cited by 49
Ant Colony Optimization for the Traveling Salesman Problem Based on Ants with Memory
Li, B., Wang, L., & Song, W. (2008) · International Conference on Natural Computation (ICNC) · doi:10.1109/icnc.2008.354 · Cited by 42
Self-citationRobust and stable gene selection via Maximum–Minimum Correntropy Criterion
Mohammadi, M., Noghabi, H. S., Hodtani, G. A., & Mashhadi, H. R. (2016) · Genomics · doi:10.1016/j.ygeno.2015.12.006 · Cited by 42
A Two-Stage Feature Selection Method for Gene Expression Data
Chuang, L., Ke, C.-H., Chang, H., & Yang, C. (2009) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2008.0083 · Cited by 39
Combined Feature Selection and Cancer Prognosis Using Support Vector Machine Regression
Sun, B., Zhu, Z.-H., Li, J., & Linghu, B. (2010) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2010.119 · Cited by 39
Wang, L., Liu, W., & Shi, H. (2008) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2007.913915 · Cited by 38
Self-citationA Blocking Strategy to Improve Gene Selection for Classification of Gene Expression Data
Bontempi, G. (2007) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2007.1014 · Cited by 37
Enhancing Learning using Feature and Example selection
Raman, B., & Ioerger, T. R. (2003) · Cited by 36
Detecting differentially expressed genes by relative entropy
Yan, X., Deng, M., Fung, W. K., & Qian, M. (2005) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2004.11.039 · Cited by 33
YamiPred: A Novel Evolutionary Method for Predicting Pre-miRNAs and Selecting Relevant Features
Kleftogiannis, D., Theofilatos, K., Likothanassis, S. D., & Mavroudi, S. P. (2015) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2014.2388227 · Cited by 32
Classification using support vector machines with graded resolution
Wang, L., Liu, B. Y., & Wan, C. (2005) · IEEE International Conference on Granular Computing (GrC) · doi:10.1109/grc.2005.1547374 · Cited by 31
Self-citationGene expression data analysis using support vector machines
Chu, F., & Wang, L. (2004) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2003.1223764 · Cited by 24
Self-citationKey node selection for containing infectious disease spread using particle swarm optimization
Fu, X., Lim, S., Wang, L., Lee, G., Ma, S., Wong, L., & Xiao, G. (2009) · IEEE Swarm Intelligence Symposium · doi:10.1109/sis.2009.4937852 · Cited by 23
Self-citationComparison of Feature Selection Methods for Cross-Laboratory Microarray Analysis
Liu, H., Peng, P.-C., Hsieh, T., Yeh, T., Lin, C., Chen, C., Hou, J., Shih, L., & Liang, D. (2013) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2013.70 · Cited by 21
Liu, J., Xu, Y., Gao, Y.-L., Zheng, C.-H., Wang, D., & Zhu, Q. (2015) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2015.2440265 · Cited by 19
Cardiovascular Genomics: A Biomarker Identification Pipeline
Phan, J. H., Quo, C. F., & Wang, M. D. (2012) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2012.2199570 · Cited by 16
Xu, X., Li, A., & Wang, M. (2015) · IET Systems Biology · doi:10.1049/iet-syb.2014.0051 · Cited by 16
Boareto, M., Cesar, J. E. D. S., Leite, V. B. P., & Caticha, N. (2014) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2014.2377750 · Cited by 14
Ranganarayanan, P., Thanigesan, N., Vivek-Ananth, R. P., Valadi, J. K., & Ramakrishnan, V. (2015) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2015.2415806 · Cited by 12
DNA Copy Number Selection Using Robust Structured Sparsity-Inducing Norms
Metsis, V., Makedon, F. S., Shen, D. G., & Huang, H. (2013) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2013.141 · Cited by 10
Network-Based Methods to Identify Highly Discriminating Subsets of Biomarkers
Sajjadi, S. J., Qian, X., Zeng, B., & Adl, A. A. (2014) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2014.2325014 · Cited by 9
Two-Step Cross-Entropy Feature Selection for Microarrays—Power Through Complementarity
Peters, T., Bulger, D. W., Loi, T.-H., et al. (2011) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2011.30 · Cited by 9
Multi objective SNP selection using pareto optimality
Gümüş, E., Görmez, Z., & Kurşun, O. (2012) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2012.12.006 · Cited by 8
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Metadata
- Authors
- Wang, L., Wang, Y., & Chang, Q.
- Venue
- Methods
- Publisher
- Elsevier BV
- Published
- 2016-12
- Volume
- 111
- Pages
- 21-31
- ISSN
- 1046-2023 (ISSN-L)
- OpenAlex
- W2516938563
- Cited by
- 331
- References
- 119
- FWCI
- 14.90
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.