A multi-filter enhanced genetic ensemble system for gene selection and sample classification of microarray data
BMC BioinformaticsPublished 1 January 2010Open access
Pengyi Yang, Bing Bing Zhou, Zili Zhang, Albert Y. Zomaya
Citations93
SJR quartileQ1
SJR score1.19
SNIP1.02
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
The experimental results indicate that the proposed multi-filter enhanced genetic ensemble (MF-GE) system is able to improve sample classification accuracy, generate more compact gene subset, and converge to the selection results more quickly.
Abstract
We used four benchmark microarray datasets (including both binary-class and multi-class classification problems) for concept proving and model evaluation. The experimental results indicate that the proposed multi-filter enhanced genetic ensemble (MF-GE) system is able to improve sample classification accuracy, generate more compact gene subset, and converge to the selection results more quickly. The MF-GE system is very flexible as various combinations of multiple filters and classifiers can be incorporated based on the data characteristics and the user preferences.
Keywords
Biochemistry, Genetics and Molecular Biology
Elsevier eBooksData Mining: Practical Machine Learning Tools and Techniques
25,718 Citations2011Ian H. Witten, Eibe Frank
C4.5: Programs for Machine Learning
23,665 Citations1992J. R. Quinlan
A complete guide to the C4.5 system as implemented in C for the UNIX environment, which starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting.
Machine LearningInduction of Decision Trees
14,815 Citations1986J. R. Quinlan
This paper summarizes an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail, which is described in detail.
ScienceMolecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring
11,622 Citations1999Todd R. Golub, Donna K. Slonim +10 more
A generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case and suggests a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.
Artificial IntelligenceWrappers for feature subset selection
8,925 Citations1997Ron Kohavi, George H. John
The wrapper method searches for an optimal feature subset tailored to a particular algorithm and a domain and compares the wrapper approach to induction without feature subset selection and to Relief, a filter approach tofeature subset selection.
Lecture notes in computer scienceEnsemble Methods in Machine Learning
7,862 Citations2000Thomas G. Dietterich
BioinformaticsA review of feature selection techniques in bioinformatics
5,289 Citations2007Yvan Saeys, Iñaki Inza +1 more
A basic taxonomy of feature selection techniques is provided, providing their use, variety and potential in a number of both common as well as upcoming bioinformatics applications.
Proceedings of the National Academy of SciencesBroad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays
4,210 Citations1999Uri Alon, Naama Barkai +5 more
A two-way clustering method is reported for analyzing a data set consisting of the expression patterns of different cell types, revealing broad coherent patterns that suggest a high degree of organization underlying gene expression in these tissues.
Artificial IntelligenceSelection of relevant features and examples in machine learning
3,295 Citations1997Avrim Blum, Pat Langley
This survey reviews work in machine learning on methods for handling data sets containing large amounts of irrelevant information and describes the advances that have been made in both empirical and theoretical work in this area.
Machine LearningTheoretical and Empirical Analysis of ReliefF and RReliefF
2,986 Citations2003Marko Robnik‐Šikonja, Igor Kononenko
How and why Relief algorithms work, their theoretical and practical properties, their parameters, what kind of dependencies they detect, how do they scale up to large number of examples and features, how to sample data for them, how robust are they regarding the noise, how irrelevant and redundant attributes influence their output and how different metrics influences them.
Journal of Bioinformatics and Computational BiologyMINIMUM REDUNDANCY FEATURE SELECTION FROM MICROARRAY GENE EXPRESSION DATA
2,794 Citations2005Chris Ding, HANCHUAN PENG
Journal of the American Statistical AssociationComparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data
2,699 Citations2002Sandrine Dudoit, Jane Fridlyand +1 more
Different discrimination methods for the classification of tumors based on gene expression data include nearest-neighbor classifiers, linear discriminant analysis, and classification trees, which are applied to datasets from three recently published cancer gene expression studies.
Nature GeneticsMLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia
1,711 Citations2001Scott A. Armstrong, Jane Staunton +8 more
It is proposed that MLL constitute a distinct disease, denoted here as MLL, and it is shown that the differences in gene expression are robust enough to classify leukemias correctly as M LL, acute lymphoblastic leukemia or acute myelogenous leukemia.
Induction of Decision Trees
999 Citations2003Quinlan
Pattern RecognitionComparison of algorithms that select features for pattern classifiers
889 Citations2000Mineichi Kudo, Jack Sklansky
It is shown that sequential floating search methods are suitable for small- and medium-scale problems and genetic algorithms are suitableFor large-scale feature selection algorithms, the goodness of a feature subset is measured by leave-one-out correct-classification rate of a nearest-neighbor (1-NN) classifier.
Molecular Biology of the CellGene Expression Patterns in Human Liver Cancers
862 Citations2002Xin Chen, ST Cheung +11 more
Using cDNA microarrays to characterize patterns of gene expression in Hepatocellular carcinoma, consistent differences between the expression patterns in HCC compared with those seen in nontumor liver tissues are found.
IEEE Transactions on Systems Man and Cybernetics - Part A Systems and HumansApplication of majority voting to pattern recognition: an analysis of its behavior and performance
842 Citations1997Louisa Lam, Shu-Ping Suen
This paper examines the mode of operation of the majority vote method in order to gain a deeper understanding of how and why it works, so that a more solid basis can be provided for its future applications to different data and/or domains.
Lecture notes in computer scienceRobust Feature Selection Using Ensemble Feature Selection Techniques
651 Citations2008Yvan Saeys, Thomas Abeel +1 more
It is shown that ensemble feature selection techniques show great promise for high-dimensional domains with small sample sizes, and provide more robust feature subsets than a single feature selection technique.
BioinformaticsGene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method
649 Citations2001Leping Li, Clarice R. Weinberg +2 more
The GA/KNN method is capable of selecting a subset of predictive genes from a large noisy data set for sample classification and is a multivariate approach that can capture the correlated structure in the data.
Computer applications in the biosciencesOptimal number of features as a function of sample size for various classification rules
449 Citations2004J. Hua, Zhe Xiong +3 more
This study employs simulation for various feature-label distributions and classification rules, and across a wide range of sample and feature-set sizes, to achieve the desired end, finding the optimal number of features as a function of sample size.
BioinformaticsClass prediction and discovery using gene microarray andproteomics mass spectroscopy data: curses, caveats, cautions
332 Citations2003Ray Somorjai, B. Dolenko +1 more
This work shows for several publicly available microarray and proteomics datasets how the 'curse of dimensionality' and dataset sparsity influence classification outcomes, and suggests an approach to assess the relative quality of apparently equally good classifiers.
BMC BioinformaticsFeature selection and classification for microarray data analysis: Evolutionary methods for identifying predictive genes
327 Citations2005Thanyaluk Jirapech-Umpai, Stuart Aitken
The major and recurring biological concepts within a collection of protein-related MEDLINE documents can be extracted by the LDA model and provide parsimonious and semantically-enriched representation of the texts in a semantic space with reduced dimensionality and can be used to index text.
Information FusionDiversity in search strategies for ensemble feature selection
323 Citations2004Alexey Tsymbal, Mykola Pechenizkiy +1 more
It is shown that, in some cases, the ensemble feature selection process can be sensitive to the choice of the diversity measure, and that the question of the superiority of a particular measure depends on the context of the use of diversity and on the data being processed.
Molecular Biology of the CellGene Expression Patterns in Human Liver Cancers
279 Citations2002X. Chen
BioinformaticsGenetic algorithms applied to multi-class prediction forthe analysis of gene expression data
261 Citations2002Chia-Huey Ooi, Patrick Tan
A GA-based gene selection scheme is described that automatically determines the members of a predictive gene group, as well as the optimal group size, that maximizes classification success using a maximum likelihood (MLHD) classification method.
Computer applications in the biosciencesHykGene: a hybrid approach for selecting marker genes for phenotype classification using microarray gene expression data
197 Citations2004Yuan Wang, Fillia Makedon +2 more
A novel hybrid approach that combines gene ranking and clustering analysis is developed that is capable of selecting relatively few marker genes while offering the same or better leave-one-out cross-validation accuracy compared with approaches that use top-ranked genes directly for classification.
BioinformaticsRankGene: identification of diagnostic genesbased on expression data
195 Citations2003Yang Su, T. M. Murali +3 more
The program integrates into one system a variety of popular ranking criteria, ranging from the traditional t-statistic to one-dimensional support vector machines, which makes RankGene a useful tool in gene expression analysis and feature selection.
Computer applications in the biosciencesMulticlass cancer classification and biomarker discovery using GA-based algorithms
188 Citations2005Jason J. Liu, Gene Cutler +6 more
It is believed that microarray-based multiclass molecular analysis can be an effective tool for cancer biomarker discovery and subsequent molecular cancer diagnosis.
BMC BioinformaticsA combinational feature selection and ensemble neural network method for classification of gene expression data
158 Citations2004Bing Liu, Qinghua Cui +2 more
BMC Medical Informatics and Decision MakingAn assessment of recently published gene expression data analyses: reporting experimental design and statistical factors
157 Citations2006Peyman Jafari, Francisco Azuaje
How Many Genes are Needed for a Discriminant Microarray Data Analysis
118 Citations2002Wentian Li, Yaning Yang
The analysis of the leukemia data from Whitehead/MIT group is a discriminant analysis, and it is observed that the performance of most of these weighted predictors on the testing set is gradually reduced as more genes are included, but a clear cutoff that separates good and bad prediction performance is not found.
BioinformaticsIdentifying differentially expressed genes from microarray experiments via statistic synthesis
98 Citations2004Yee Hwa Yang, Yuanyuan Xiao +1 more
A new approach is presented, which addresses both ranking and selection of differentially expressed genes, and integrates differing statistics via a distance synthesis scheme, while achieving robustness properties lacked by the individual statistics.
Journal of Intelligent & Fuzzy SystemsGene selection by sequential search wrapper approaches in microarray cancer class prediction
84 Citations2002Iñaki Inza, Basilio Sierra +2 more
By the use of the gene selection procedure, the accuracy of supervised algorithms is significantly improved and the number of genes of the classification models is notably reduced for all datasets.
Lecture notes in computer scienceApplication of the Evolutionary Algorithms for Classifier Selection in Multiple Classifier Systems with Majority Voting
44 Citations2001Dymitr Ruta, Bogdan Gabryś
This work intends to show empirically, that using efficient evolutionary-based selection leads to the results comparable to absolutely best, found exhaustively.
An Ensemble of Classifiers with Genetic Algorithm Based Feature Selection
38 Citations2008Zili Zhang, Pengyi Yang
This paper proposes a novel hybrid algorithm, which is the combination of a multi-objective Genetic Algorithm (GA) and an ensemble classifier to improve the overall sample classifica tion accuracy while also identifying the most important features in the dataset of interest.
IEEE/ACM Transactions on Computational Biology and BioinformaticsA Blocking Strategy to Improve Gene Selection for Classification of Gene Expression Data
37 Citations2007Gianluca Bontempi
The paper shows that the blocking strategy significantly improves the performance of a conventional forward selection for a set of 16 publicly available cancer expression data sets and two further validations based on available biological annotation support the claim that blocking strategies in feature selection may improve the accuracy and the quality of the solution.
BMC BioinformaticsVisualization-based discovery and analysis of genomic aberrations in microarray data
18 Citations2005Chad L. Myers, Xing Chen +1 more
ChARMView is an effective and accurate visualization and analysis system for recognizing even small aneuploidies or subtle expression biases, identifying recurring aberrations in sets of experiments, and pinpointing functionally relevant copy number changes.
IEEE Intelligent SystemsAn Agent-Based Hybrid System for Microarray Data Analysis
17 Citations2009Zili Zhang, Pengyi Yang +2 more
It is demonstrated that agent-based approaches are suitable for building hybrid systems in general and that a genetic ensemble system is appropriate for microarray data analysis in particular.
BMC BioinformaticsA voting approach to identify a small number of highly predictive genes using multiple classifiers
15 Citations2009Md. Rafiul Hassan, M. Maruf Hossain +4 more
It is shown that it is possible to obtain superior classification accuracy with this approach and obtain a compact gene set that is also biologically relevant and has good coverage of different biological processes.