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Penalized feature selection and classification in bioinformatics
2008
Briefings in Bioinformatics
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In this article, we provide a review of several recently developed penalized feature selection and classification techniquesçwhich belong to the family of embedded feature selection methodsçfor bioinformatics ...
Our goal is to make interested researchers aware of these feature selection and classification methods that are applicable to high-dimensional bioinformatics data. ...
Acknowledgements The authors thank the editor and three anonymous referees for their insightful comments, which have led to significant improvement of this article. ...
doi:10.1093/bib/bbn027
pmid:18562478
pmcid:PMC2733190
fatcat:mjiquxmuz5ftnkvvv3soqxj4bq
A Multi-task Feature Selection Filter for Microarray Classification
2009
2009 IEEE International Conference on Bioinformatics and Biomedicine
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A major challenge in microarray classification and biomarker discovery is dealing with small-sample high-dimensional data where the number of genes used as features is typically orders of magnitude larger ...
Comparison of the classification accuracies reveals that the multi-task feature selection is superior to single-task feature selection. ...
Penalized Logistic Regression for Multi-Task Learning The multi-task feature selection filter proposed in Section 4 could in principle be used in conjunction with any single-task or multi-task classification ...
doi:10.1109/bibm.2009.79
dblp:conf/bibm/LanV09
fatcat:h2umzg2tzvgk3aw73skylrbd4e
A Review of Matched-pairs Feature Selection Methods for Gene Expression Data Analysis
2018
Computational and Structural Biotechnology Journal
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In this review, we compare the performance of 10 feature-selection methods (eight MPFS methods and two traditional unpaired methods) on two real datasets by applied three classification methods, and analyze ...
Although numerous methods have been developed for feature selection in bioinformatics, it is still a challenge to choose the appropriate methods for a specific problem and seek for the most reasonable ...
Feature selection, a.k.a. Variable selection or gene selection (in bioinformatics), is the process of selecting a subset of relevant features for model construction or interpretation of results. ...
doi:10.1016/j.csbj.2018.02.005
pmid:30275937
pmcid:PMC6158772
fatcat:6fe55oobwnejlhk6kgku2qtmmy
Correction: Class Prediction and Feature Selection with Linear Optimization for Metagenomic Count Data
2014
PLoS ONE
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Sparse distance based learning for simultaneous multiclass classification and feature selection of metagenomic data. Bioinformatics 27(23): 3242-3249. ...
The description of the methods that overlap with that in the publication in Bioinformatics relates to the first paragraphs of the Methods section before the section titled and Penalized SVM Methods. ...
doi:10.1371/journal.pone.0097958
fatcat:3doay74b6zeefniug2ymg3oesm
Analysis of recursive gene selection approaches from microarray data
2005
Bioinformatics
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We answer this question by examining multiple classifers (SVM, ridge regression and Rocchio) with feature selection in recursive and non-recursive settings on three DNA microarray datasets (ALL-AML Leukemia ...
A further analysis of the experimental results shows that different classifiers penalize redundant features to different extent and this property plays an important role in the recursive feature selection ...
the choice of the classification method and wrapper approaches, meaning that a classifier is used to generate scores for features in the selection process and feature selection depends on the choice of ...
doi:10.1093/bioinformatics/bti618
pmid:16118263
fatcat:sq3bm4r26rfr7o7om6xqs2wemy
pathClass: an R-package for integration of pathway knowledge into support vector machines for biomarker discovery
2011
Bioinformatics
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Here, we introduce pathClass, a collection of different SVM-based classification methods for improved gene selection and classfication performance. ...
Different groups have recently shown that the usage of prior biological knowledge significantly improves the classification results in terms of accuracy as well as reproducibility and interpretability ...
This variable selection is archieved by penalizing the SVM objective function with an F ∞ -norm, instead of the commonly used L 1 or L 2 penalization. ...
doi:10.1093/bioinformatics/btr157
pmid:21450711
fatcat:kdfiuqwwuzgtbkdu35c46lr3xi
On the parameter optimization of Support Vector Machines for binary classification
2012
Journal of Integrative Bioinformatics
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Techniques for feature selection and SVM parameters optimization are known to improve classification accuracy, and its literature is extensive.In this paper we review the strategies that are used to improve ...
the classification performance of SVMs and perform our own experimentation to study the influence of features and hyper-parameters in the optimization process, using several known kernels. ...
Adding feature selection to C (SP3) diminishes this effect and further improves classification. ...
doi:10.1515/jib-2012-201
fatcat:ozphynvf2jd77lxawbampf72wa
GenePEN: analysis of network activity alterations in complex diseases via the pairwise elastic net
2015
Statistical Applications in Genetics and Molecular Biology
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We describe GenePEN, a penalized logistic regression approach for sample classification via convex optimization, using a newly designed Pairwise Elastic Net penalty that favors the selection of discriminative ...
Identifying such interconnected and jointly altered gene/protein groups from functional omics data and a given molecular interaction network is a key challenge in bioinformatics. ...
Moreover, in the Supplementary Material feature selection results are evaluated on simulated data, and GenePEN consistently achieved similar or better performance in selecting truly differentially expressed ...
doi:10.1515/sagmb-2014-0045
pmid:25720129
fatcat:2m3la542kvcaznwumoya6qtwiu
Microarray classification with hierarchical data representation and novel feature selection criteria
2012
2012 IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE)
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Both problem are assessed by a two-step approach applying hierarchical clustering to create new features called metagenes and introducing a novel feature ranking criterion, inside the wrapper feature selection ...
An effective methodology to output a precise and reliable classifier is proposed in this work as an improvement of the algorithm in [1] . ...
ACKNOWLEDGEMENTS This work has been partially financed by "Fundació privada CELLEX"; and the "Departament d'Universitats, Recerca i Societat de la Informació de la Generalitat de Catalunya". ...
doi:10.1109/bibe.2012.6399648
dblp:conf/bibe/BosioPSO12
fatcat:zw25um5sfndlzefwgbohq4efja
Feature selection in bioinformatics
2012
Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering X
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In this paper, we investigate some powerful feature selection techniques and apply them to problems in bioinformatics. ...
In bioinformatics, there are often a large number of input features. ...
CONCLUSIONS In this paper, we demonstrated the effectiveness of feature selection in bioinformatics through 2 particular problems, i.e., population classification with SNPs and cancer classification with ...
doi:10.1117/12.921417
fatcat:c3oazrvmenhjrk57h3ylfw7imi
A selective review of robust variable selection with applications in bioinformatics
2014
Briefings in Bioinformatics
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In this article, we provide a selective review of robust penalized variable selection approaches especially designed for high-dimensional data from bioinformatics and biomedical studies. ...
Application examples of the robust penalization approaches in representative bioinformatics and biomedical studies are also illustrated. ...
Acknowledgements The authors thank the editor and three reviewers for careful review and insightful comments, which have led to significant improvement of this manuscript. ...
doi:10.1093/bib/bbu046
pmid:25479793
pmcid:PMC4570200
fatcat:5yflco36i5gixoroi27fpiove4
Semi-supervised learning via penalized mixture model with application to microarray sample classification
2006
Bioinformatics
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The penalized mixture model seems to be promising for high-dimensional data with the capability of novel class discovery and automatic feature selection. ...
A simulation study confirmed that, compared to the standard mixture model with or without initial variable selection, the penalized mixture model performed much better in identifying relevant genes and ...
ACKNOWLEDGEMENT WP was supported by NIH grant HL65462 and a UM AHC Development grant, and AJ and RH by NIH grant P01-HL076540. The authors thank the reviewers for helpful and constructive comments. ...
doi:10.1093/bioinformatics/btl393
pmid:16870935
fatcat:i2iymc5jyze3znb6z2f5in3sgi
Improving Penalized Logistic Regression Model with Missing Values in High-Dimensional Data
2022
International Journal of Online and Biomedical Engineering (iJOE)
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In high-dimensional data, penalized regression is a popular technique for performing feature selection and coefficient estimation simultaneously. ...
Therefore, this study uses imputations penalized regression models as an extension of the penalized methods to improve the performance and impute missing values in high-dimensional data. ...
The penalized method is used to select features and classify them. ...
doi:10.3991/ijoe.v18i02.25047
fatcat:ixkyuslvsrf7tcctrxy5thimhy
Guest Editors' Introduction to the Special Issue: Machine Learning for Bioinformatics-Part 1
2005
IEEE/ACM Transactions on Computational Biology & Bioinformatics
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In the context of gene expression analysis, effective attribute selection and classification methods have been a focus of study in recent years. ...
In the paper "Attribute Clustering for Grouping, Selection, and Classification of Gene Expression Data" by Wai-Ho Au, Keith C.C. Chan, Andrew K.C. ...
doi:10.1109/tcbb.2005.25
fatcat:4ks6pssulrfkjfrqsfngrqktiy
Elastic SCAD as a novel penalization method for SVM classification tasks in high-dimensional data
2011
BMC Bioinformatics
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Classification and variable selection play an important role in knowledge discovery in highdimensional data. ...
selection and therefore a number of feature selection procedures have been developed. ...
GT and AB participated in the preparation of the manuscript. AB and PL supervised the work. All authors read and approved the manuscript. ...
doi:10.1186/1471-2105-12-138
pmid:21554689
pmcid:PMC3113938
fatcat:hmuooixjl5d6lnmuhczcytyf24
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