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An Enhanced Recursive Firefly Algorithm for Informative Gene Selection

INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2019年第2期10卷 21-33页

作者： Dif, Nassima Elberrichi, Zakaria Univ Djillali Liabes Sidi Bel Abbes EEDIS Lab Sidi Bel Abbes Algeria

Feature selection is the process of identifying good performing combinations of significant features among many possibilities. This preprocess improves the classification accuracy and facilitates the learning task. For this optimization problem, the authors have used a metaheuristics approach. Their main objective is to propose an enhanced version of the firefly algorithm as a wrapper approach by adding a recursive behavior to improve the search of the optimal solution. They applied SVM classifier to investigate the proposed method. For the authors experimentations, they have used the benchmark microarray datasets. The results show that the new enhanced recursive FA (RFA) outperforms the standard version with a reduction of dimensionality for all the datasets. As an example, for the leukemia microarray dataset, they have a perfect performance score of 100% with only 18 informative selected genes among the 7,129 of the original dataset. The RFA was competitive compared to other state-of-art approaches and achieved the best results for CNS, Ovarian cancer, MLL, prostate, Leukemia_4c, and lymphoma datasets.

关键词： Classification Data Mining Data Preprocessing Data Reduction Feature Selection Firefly Algorithm Metaheuristic microarray datasets Recursion SVM Algorithm

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Differential gene expression in disease: a comparison between high-throughput studies and the literature

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BMC MEDICAL GENOMICS 2017年第1期10卷 1-10页

作者： Rodriguez-Esteban, Raul Jiang, Xiaoyu Roche Innovat Ctr Basel Roche Pharmaceut Res & Early Dev Grenzacherstr 124 CH-4070 Basel Switzerland Biogen Cambridge MA USA

Background: Differential gene expression is important to understand the biological differences between healthy and diseased states. Two common sources of differential gene expression data are microarray studies and the biomedical literature. Methods: With the aid of text mining and gene expression analysis we have examined the comparative properties of these two sources of differential gene expression data. Results: The literature shows a preference for reporting genes associated to higher fold changes in microarray data, rather than genes that are simply significantly differentially expressed. Thus, the resemblance between the literature and microarray data increases when the fold-change threshold for microarray data is increased. Moreover, the literature has a reporting preference for differentially expressed genes that (1) are overexpressed rather than underexpressed;(2) are overexpressed in multiple diseases;and (3) are popular in the biomedical literature at large. Additionally, the degree to which diseases are similar depends on whether microarray data or the literature is used to compare them. Finally, vaguely-qualified reports of differential expression magnitudes in the literature have only small correlation with microarray fold-change data. Conclusions: Reporting biases of differential gene expression in the literature can be affecting our appreciation of disease biology and of the degree of similarity that actually exists between different diseases.

关键词： microarray FC Differential Gene Expression Data Differentially Expressed Genes (DEG) microarray datasets Gene Regulation Ontology

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Modified PSO based Feature Selection for microarray Data Classification

Modified PSO based Feature Selection for Microarray Data Cla...

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IEEE Power, Communication and Information Technology Conference (PCITC)

作者： Mohapatra, Puspanjali Chakravarty, S. IIIT Bhubaneswar Dept CSE Bhubaneswar Orissa India OEC Bhubaneswar Dept CSE Bhubaneswar Orissa India

ISBN: (纸本)9781479974559

The main goal of successful microarray data classification is to reduce the computational time while improving the classification accuracy. Though a large pool of techniques are already available, accurate classification of normal and malignant tissue cells is very challenging for the diagnosis of various types of cancers in humans. In this paper, Support Vector Machines (SVM), Naive Bayesian and k-Nearest neighbor classifiers are used for classification of publicly available biomedical microarray datasets such as Prostate cancer, Leukemia and Colon tumor. To overcome the curse of dimensionality, Modified Particle Swarm Optimization (MPSO) is used to select the features from the datasets. A number of useful performance evaluation measures including classification accuracy, precision, recall, F-score as well as the area under the receiver operating characteristic curve are taken to evaluate the classifiers. After analyzing the experimental results, it is verified that SVM outperforms other classifiers and the performance is even improved a lot after feature selection.

关键词： microarray datasets Support Vector Machine Particle Swarm Optimization (PSO) Receiver Operating characteristic (ROC) curve

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An integrated cancer biomarker information system

An integrated cancer biomarker information system

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27th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society

作者： Feng, W. Wu, B. Phan, J. Dale, J. Young, A. N. Wang, M. D. Georgia Inst Technol Dept Biomed Engn Atlanta GA 30332 USA

ISBN: (纸本)0780387406

Rapid progress in functional genomics is yielding unparalleled opportunities for cancer biomarker discovery. Several procedures including data collection, analysis, verification, and visualization are necessary to identify cancer biomarkers from continuously updated microarray data, which are derived from multiple tissue sources using various methods. BioMarker is designed to integrate these procedures in a user-friendly manner, for practical application by cancer researcher and clinicians. From the feedback of clinical and research application, BioMarker is the first effort to provide a system that facilitates all aspects of biomarker discovery from microarray datasets.

关键词： cancer biomarker microarray datasets database system J2EE

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Improving Classification Performance of microarray Analysis by Feature Selection and Feature Extraction Methods

Improving Classification Performance of Microarray Analysis ...

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作者： Jing Sun Laurentian University

学位级别：硕士

In this study, we compared two feature extraction methods (PCA, PLS) and seven feature selection methods (mRMR and its variations, MaxRel, QPFS) on four different classifiers (SVM, RF, KNN, NN). We use ratio comparison validation for PCA method and 10-folds cross validation method for both the feature extraction and feature selection methods. We use Leukemia data set and Colon data set to apply the combinations and measured accuracy as well as area under ROC. The results illustrated that feature selection and extraction methods can both somehow improve the performance of classification tasks on microarray data sets. Some combinations of classifier and feature preprocessing method can greatly improve the accuracy as well as the AUC value are given in this study.

关键词： microarray datasets Feature Extraction Feature Selection Principal Component Analysis Partial Least Square minimum Redundancy-Maximum Relevant Quadratic Programming Feature Selection Support Vector Machine Random Forest k-Nearest-Neighbor Neural Network

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Cross-platform method for identifying candidate network biomarkers for prostate cancer

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IET SYSTEMS BIOLOGY 2009年第6期3卷 505-512页

作者： Jin, G. Zhou, X. Cui, K. Zhang, X. -S. Chen, L. Wong, S. T. C. Methodist Hosp Med Syst Biol Lab Ctr Bioinformat & Biotechnol Res InstWeill Cornell Med Coll Houston TX 77030 USA Chinese Acad Sci Acad Math & Syst Sci Beijing 100080 Peoples R China Osaka Sangyo Univ Dept Elect Engn Osaka 5748530 Japan

Discovering biomarkers using mass spectrometry (MS) and microarray expression profiles is a promising strategy in molecular diagnosis. Here, the authors proposed a new pipeline for biomarker discovery that integrates disease information for proteins and genes, expression profiles in both genomic and proteomic levels, and protein-protein interactions (PPIs) to discover high confidence network biomarkers. Using this pipeline, a total of 474 molecules (genes and proteins) related to prostate cancer were identified and a prostate-cancer-related network (PCRN) was derived from the integrative information. Thus, a set of candidate network biomarkers were identified from multiple expression profiles composed by eight microarray datasets and one proteomics dataset. The network biomarkers with PPIs can accurately distinguish the prostate patients from the normal ones, which potentially provide more reliable hits of biomarker candidates than conventional biomarker discovery methods.

关键词： biological organs mass spectroscopic chemical analysis proteins Biology and medical computing genomic level medical computing biomarkers mass spectrometry tumours molecular diagnosis microarray expression profile microarray datasets Biomolecular structure, configuration, conformation, and active sites cross-platform method Mass spectrometry (chemical analysis) Genomic techniques Physics of subcellular structures prostate cancer genomics Proteomic techniques Biomolecular interactions, charge transfer complexes cancer proteomics gene expression disease information protein-protein interactions genetics Biomedical engineering

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The imbalance problem: A comparison of sampling approaches using different parameters and feature selection methods in the context of classification

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EXPERT SYSTEMS 2024年第8期41卷 e13591-e13591页

作者： Morillo-Salas, Jose L. Bolon-Canedo, Veronica Alonso-Betanzos, Amparo Univ A Coruna Res Ctr Informat & Commun Technol CIT La Coruna Spain Univ A Coruna Res Ctr Informat & Commun Technol CIT Campus Elvina La Coruna 15071 Spain

A common situation in classification tasks is to deal with unbalanced datasets, an issue that appears when the majority class(es) has a large number of samples compared to the minority class(es). This problem is even more significant when the datasets have a large number of features but only a few samples, as is the case with microarray datasets. Traditionally, an approach to alleviate this problem has been the application of sampling methods to obtain more balanced classes, increasing the number of samples in the minority class (replicating samples or generating new synthetic samples), or decreasing the number of samples in the majority class. In this study, we have compared different balancing methods, including a novel method that applies sampling in both the minority and majority classes. The interest in applying feature selection in combination with balancing methods has also been explored. In view of the results, a recommendation of sampling method, feature selection, and classifier is proposed to improve the classification results according to the type of dataset.

关键词： classification feature selection microarray datasets oversampling unbalanced datasets

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Gene expression rate comparison for multiple high-throughput datasets

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IET SYSTEMS BIOLOGY 2013年第5期7卷 135-142页

作者： Chen, Chien-Ming Shih, Tsan-Huang Pai, Tun-Wen Liu, Zhen-Long Chang, Margaret Dah-Tsyr Hu, Chin-Hwa Natl Taiwan Ocean Univ Dept Comp Sci & Engn Keelung 20224 Taiwan Natl Taiwan Ocean Univ Ctr Excellence Marine Bioenvironm & Biotechnol Keelung 20224 Taiwan Natl Tsing Hua Univ Inst Mol & Cellular Biol Hsinchu 30013 Taiwan Natl Tsing Hua Univ Dept Med Sci Hsinchu 30013 Taiwan Natl Taiwan Ocean Univ Inst Biosci & Biotechnol Keelung 20224 Taiwan

microarray provides genome-wide transcript profiles, whereas RNA-seq is an alternative approach applied for transcript discovery and genome annotation. Both high-throughput techniques show quantitative measurement of gene expression. To explore differential gene expression rates and understand biological functions, the authors designed a system which utilises annotations from Kyoto Encyclopedia of Genes and Genomes (KEGG) biological pathways and Gene Ontology (GO) associations for integrating multiple RNA-seq or microarray datasets. The developed system is initiated by either estimating gene expression levels from mapping next generation sequencing short reads onto reference genomes or performing intensity analysis from microarray raw images. Normalisation procedures on expression levels are evaluated and compared through different approaches including Reads Per Kilobase per Million mapped reads (RPKM) and housekeeping gene selection. Such gene expression levels are shown in different colour shades and graphically displayed in designed temporal pathways. To enhance importance of functional relationships of clustered genes, representative GO terms associated with differentially expressed gene cluster are visually illustrated in a tag cloud representation.

关键词： genetics genomics lab-on-a-chip molecular biophysics molecular configurations RNA gene expression rate multiple high-throughput datasets genome-wide transcript profiles transcript discovery genome annotation high-throughput techniques quantitative measurement genome biological pathways gene ontology associations microarray datasets multiple RNA-seq datasets gene expression levels next generation sequencing microarray raw images housekeeping gene selection colour shades temporal pathways representative GO terms gene cluster tag cloud representation

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Quantum-Inspired Owl Search Algorithm with Ensembles of Filter Methods for Gene Subset Selection from microarray Data

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2023年第2期37卷 2351001-2351001页

作者： Mandal, Ashis Kumar Sen, Rikta Chakraborty, Basabi Iwate Prefectural Univ Grad Sch Software & Informat Sci Iwate Japan Hajee Mohammad Danesh Sci & Technol Univ Dept Comp Sci & Engn Dinajpur Bangladesh Iwate Prefectural Univ Reg Res Cooperat Ctr Iwate Japan Madanapalle Inst Technol & Sci Madanapalle Andhra Pradesh India

Finding the optimum subset of genes for microarray classification is laborious because microarray data are often high-dimensional and contain many irrelevant and redundant genes. To overcome this problem, we have proposed a two-step technique. In the first step, to reduce the vast number of genes or features, an ensemble of popular rank-based feature selection algorithms with filter evaluation metrics are used to select a group of top-ranking genes. In the next step, the quantum-inspired owl search algorithm (QIOSAf), a new filter fitness function-based metaheuristic search technique incorporating concepts from quantum computing, is developed to identify the best subset of genes from the predetermined list. The experimental findings reveal that the ensemble approach in the first step can select more dominant groups of genes than each of the individual filters. Furthermore, it has been found that QIOSAf can reduce the cardinality of the selected optimum gene subset with comparable classification accuracy and requires lesser computational time than our earlier proposed QIOSA-based wrapper approach (i.e. QIOSAw). Besides, compared with three popular evolutionary feature subset selection algorithms, QIOSAf efficiently reduces the optimum cardinality of the gene subset while maintaining acceptable classification accuracy.

关键词： microarray datasets optimum gene subset ensemble approach metaheuristics search owl search algorithm quantum-inspired computing

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Identification of candidate genes of nasopharyngeal carcinoma by bioinformatical analysis

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ARCHIVES OF ORAL BIOLOGY 2019年 106卷 104478-000页

作者： Ye, Zhimin Wang, Fangzheng Yan, Fengqin Wang, Lei Li, Bin Liu, Tongxin Hu, Fujun Jiang, Mingxiang Fu, Zhenfu Chinese Acad Sci Inst Canc Res & Basic Med Sci 1 Banshan East Rd Hangzhou 310022 Zhejiang Peoples R China Univ Chinese Acad Sci Canc Hosp 1 Banshan East Rd Hangzhou 310022 Zhejiang Peoples R China Zhejiang Canc Hosp Dept Radiat Oncol 1 Banshan East Rd Hangzhou 310022 Zhejiang Peoples R China Zhejiang Canc Hosp Dept Radiol 1 Banshan East Rd Hangzhou 310022 Zhejiang Peoples R China

Objective: This study aimed to identify candidate genes as potential biomarkers in nasopharyngeal carcinoma (NPC) by bioinformatical analysis. Methods: Three microarray datasets: GSE32906, GSE15170, GSE53819 were download from public database and analyzed to identify the differentially expressed genes (DEGs) between NPC and normal samples. Functional and pathway enrichment analysis of the DEGs were performed. Protein-protein interaction network and gene-transcription factor regulatory network of DEGs were constructed. And the expression of hub genes in NPC was also validated based on the public database. Results: A total of 16 up-regulated and 27 down-regulated genes were screened out from the microarray datasets. Functional and pathway enrichment analysis showed that DEGs were mostly enriched in positive regulation of angiogenesis, mesenchymal cell proliferation, cell surface and DNA binding, ECM-receptor interaction pathway, PI3K-Akt signaling pathway, and pathways in cancer. Five hub genes JUN, VEGFA, FOXM1, MYB, and WNT5A were identified from the protein-protein interaction network. Subsequently, the hub gene-transcription factor regulatory network revealed that STAT3, MYC, SOX2, RUNX2 present key relations with hub genes. The expression of these five hub genes were also validated to be differentially expressed among NPC and normal samples. Conclusions: The current study indicated that the hub DEGs JUN, VEGFA, FOXM1, MYB, and WNT5A we identified might be potential therapeutic biomarkers of NPC.

关键词： Nasopharyngeal carcinoma microarray datasets Differentially expressed genes Bioinformatical analysis

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