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microarray analysis Workflow Based on a Genetic Algorithm to Discover Potential Hub Genes

CURRENT BIOINFORMATICS 2022年第9期17卷 787-792页

作者： Carballido, Jessica Andrea UNS Inst CS & Engn Dept CS & Engn CONICET Bahia Blanca Argentina

This paper presents a sequence of steps oriented to gain biological knowledge from microarray gene expression data. The pipeline's core is a canonical multi-objective Genetic Algorithm (GA), which takes a gene expression matrix and a factor as input. The factor groups samples according to different criteria, e.g., healthy tissue and diseased tissue samples. The result of one run of the GA is a gene set with good properties both at the individual level, in terms of differential expression, and at the aggregate level, in terms of correlation between expression profiles. microarray experiment data are obtained from GEO (Gene Expression Omnibus dataset). As for the pipeline structure, independent runs of the GA are analyzed, genes in common between all the runs are collected, and over-representation analysis is performed. At the end of the process, a small number of genes of interest arise. The methodology is exemplified with a leukemia benchmark dataset, and a group of genes of interest is obtained for the illustrative example.

关键词： Functional genomics genetic algorithm microarray data analysis gene ontology over-representation analysis mutual information

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Feature selection for multi-class problems by using pairwise-class and all-class techniques

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INTERNATIONAL JOURNAL OF GENERAL SYSTEMS 2011年第4期40卷 381-394页

作者： You, Mingyu Li, Guo-Zheng Tongji Univ Dept Control Sci & Engn Shanghai 201804 Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing 210093 Peoples R China

Feature selection has been a key technology in massive data processing, e. g. in microarray data analysis with few samples but high-dimensional genes. One common problem in multi-class microarray data analysis is the unbalanced recognition or prediction accuracies among classes, which usually leads to poor system performance. One of the main reasons is the unfair feature (gene) selection method. In this paper, a novel feature selection framework by using pairwise-class and all-class techniques (namely FrPA) is proposed to balance the performance among classes and improve the average accuracy. The feature (gene) rank list on all classes and the lists on each pair of classes are all taken into consideration during feature selection. The strategy of round-robin is embedded into the framework to select final features from the different rank lists. Experimental results on several microarray data sets show that FrPA helps to achieve higher classification accuracy and balance the performance among classes.

关键词： feature selection pairwise-class all-class round-robin microarray data analysis

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ExPlain™: finding upstream drug targets in disease gene regulatory networks

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SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2008年第5-6期19卷 481-494页

作者： Kel, A. Voss, N. Valeev, T. Stegmaier, P. Kel-Margoulis, O. Wingender, E. BIOBASE GmbH Wolfenbuttel Germany Inst Chem Biol & Fundamental Med Novosibirsk Russia BoRainbow Inc Novosibirsk Russia Univ Gottingen UKG Dept Bioinformat D-3400 Gottingen Germany

Different signal transduction pathways leading to the activation of transcription factors (TFs) converge at key molecules that master the regulation of many cellular processes. Such crossroads of signalling networks often appear as Achilles Heels causing a disease when not functioning properly. Novel computational tools are needed for analysis of the gene expression data in the context of signal transduction and gene regulatory pathways and for identification of the key nodes in the networks. An integrated computational system, ExPlain (TM) (***) was developed for causal interpretation of gene expression data and identification of key signalling molecules. The system utilizes data from two databases (TRANSFAC (R) and TRANSPATH (R)) and integrates two programs: (1) Composite Module Analyst (CMA) analyses 5'-upstream regions of co-expressed genes and applies a genetic algorithm to reveal composite modules (CMs) consisting of co-occurring single TF binding sites and composite elements;(2) ArrayAnalyzer (TM) is a fast network search engine that analyses signal transduction networks controlling the activities of the corresponding TFs and seeks key molecules responsible for the observed concerted gene activation. ExPlain (TM) system was applied to microarray data on inflammatory bowel diseases (IBD). The results obtained suggest a number of highly interesting biological hypotheses about molecular mechanisms of pathological genetic disregulation.

关键词： microarray data analysis promoter analysis transcription factors composite elements signal transduction drug targets

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AUCTSP: an improved biomarker gene pair class predictor

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BMC BIOINFORMATICS 2018年第1期19卷 1-13页

作者： Kagaris, Dimitri Khamesipour, Alireza Yiannoutsos, Constantin T. Southern Illinois Univ Dept Elect & Comp Engn 1230 Lincoln Dr Carbondale IL 62901 USA Indiana Univ Dept Biostat Sch Publ Hlth 410 West 10th StSuite 3000 Indianapolis IN 46202 USA

Background: The Top Scoring Pair (TSP) classifier, based on the concept of relative ranking reversals in the expressions of pairs of genes, has been proposed as a simple, accurate, and easily interpretable decision rule for classification and class prediction of gene expression profiles. The idea that differences in gene expression ranking are associated with presence or absence of disease is compelling and has strong biological plausibility. Nevertheless, the TSP formulation ignores significant available information which can improve classification accuracy and is vulnerable to selecting genes which do not have differential expression in the two conditions ("pivot" genes). Results: We introduce the AUCTSP classifier as an alternative rank-based estimator of the magnitude of the ranking reversals involved in the original TSP. The proposed estimator is based on the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) and as such, takes into account the separation of the entire distribution of gene expression levels in gene pairs under the conditions considered, as opposed to comparing gene rankings within individual subjects as in the original TSP formulation. Through extensive simulations and case studies involving classification in ovarian, leukemia, colon, breast and prostate cancers and diffuse large b-cell lymphoma, we show the superiority of the proposed approach in terms of improving classification accuracy, avoiding overfitting and being less prone to selecting non-informative (pivot) genes. Conclusions: The proposed AUCTSP is a simple yet reliable and robust rank-based classifier for gene expression classification. While the AUCTSP works by the same principle as TSP, its ability to determine the top scoring gene pair based on the relative rankings of two marker genes across all subjects as opposed to each individual subject results in significant performance gains in classification accuracy. In addition, the proposed method tends to avoid selec

关键词： microarray data analysis Gene expression Gene selection Receiver operating characteristic (ROC) curve AUC Leukemia Breast cancer Ovarian cancer Colon cancer Prostate cancer Diffuse large B-Cell lymphoma

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Finding rule groups to classify high dimensional gene expression datasets

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COMPUTATIONAL BIOLOGY AND CHEMISTRY 2009年第1期33卷 108-113页

作者： An, Jiyuan Chen, Yi-Ping Phoebe Deakin Univ Fac Sci & Technol Melbourne Vic 3125 Australia

microarray data provides quantitative information about the transcription profile of cells. To analyze microarray datasets, methodology of machine learning has increasingly attracted bioinformatics researchers. Some approaches of machine learning are widely used to classify and minebiological datasets. However, many gene expression datasets are extremely high dimensionality, traditional machine learning methods cannot be applied effectively and efficiently. This paper proposes a robust algorithm to find out rule groups to classify gene expression datasets. Unlike the most classification algorithms, which select dimensions (genes) heuristically to form rules groups to identify classes such as cancerous and normal tissues, our algorithm guarantees finding out best-k dimensions (genes) to form rule groups for the classification of expression datasets. Our experiments show that the rule groups obtained by our algorithm have higher accuracy than that of other classification approaches. (c) 2008 Elsevier Ltd. All rights reserved.

关键词： Gene expression datasets microarray data analysis Classification

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Significance and Functional Similarity for Identification of Disease Genes

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2017年第6期14卷 1419-1433页

作者： Maji, Pradipta Shah, Ekta Indian Stat Inst Biomed Imaging & Bioinformat Lab Machine Intelligence Unit Kolkata 700108 India

One of the most significant research issues in functional genomics is insilico identification of disease related genes. In this regard, the paper presents a new gene selection algorithm, termed as SiFS, for identification of disease genes. It integrates the information obtained from interaction network of proteins and gene expression profiles. The proposed SiFS algorithm culls out a subset of genes from microarray data as disease genes by maximizing both significance and functional similarity of the selected gene subset. Based on the gene expression profiles, the significance of a gene with respect to another gene is computed using mutual information. On the other hand, a new measure of similarity is introduced to compute the functional similarity between two genes. Information derived from the protein-protein interaction network forms the basis of the proposed SiFS algorithm. The performance of the proposed gene selection algorithm and new similarity measure, is compared with that of other related methods and similarity measures, using several cancer microarray data sets.

关键词： Disease gene identification microarray data analysis feature selection protein-protein interaction network

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Using prior knowledge in the inference of gene association networks

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APPLIED INTELLIGENCE 2020年第11期50卷 3882-3893页

作者： Neponnuceno-Channorro, Isabel A. Neponnuceno, Juan A. Luis Galvan-Rojas, Jose Vega-Marquez, Belen Rubio-Escudero, Cristina Univ Seville Dept Lenguajes & Sistemas Informat Seville Spain

Traditional computational techniques are recently being improved with the use of prior biological knowledge from open-access repositories in the area of gene expression data analysis. In this work, we propose the use of prior knowledge as heuristic in an inference method of gene-gene associations from gene expression profiles. In this paper, we use Gene Ontology, which is an open-access ontology where genes are annotated using their biological functionality, as a source of prior knowledge together with a gene pairwise Gene-Ontology-based measure. The performance of our proposal has been compared to other benchmark methods for the inference of gene networks, outperforming in some cases and obtaining similar and competitive results in others, but with the advantage of providing simple and interpretable models, which is a desired feature for the Artificial Intelligence Health related models as stated by the European Union.

关键词： Gene-gene association networks Ontology Semantic similarity measure Information fusion microarray data analysis

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Modified fuzzy gap statistic for estimating preferable number of clusters in fuzzy k-means clustering

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JOURNAL OF BIOSCIENCE AND BIOENGINEERING 2008年第3期105卷 273-281页

作者： Arima, Chinatsu Hakamada, Kazumi Okamoto, Masahiro Hanai, Taizo Kyushu Univ Grad Sch Syst Life Sci Higashi Ku Fukuoka 8128581 Japan

In clustering methods, the estimation of the optimal number of clusters is significant for subsequent analysis. Without detailed biological information on the genes involved, the evaluation of the number of clusters becomes difficult, and we have to rely on an internal measure that is based on the distribution of the data of the clustering result. The Gap statistic has been proposed as a superior method for estimating the number of clusters in crisp clustering. In this study, we proposed a modified Fuzzy Gap statistic (MFGS) and applied it to fuzzy k-means clustering. For estimating the number of clusters, fuzzy k-means clustering with the MFGS was applied to two artificial data sets with noise and to two experimentally observed gene expression data sets. For the artificial data sets, compared with other internal measures, the MFGS showed a higher performance in terms of robustness against noise for estimating the optimal number of clusters. Moreover, it could be used to estimate the optimal number of clusters in experimental data sets. It was confirmed that the proposed MFGS is a useful method for estimating the number of clusters for microarray data sets.

关键词： clustering validity index fuzzy k-means microarray data analysis estimation of number of clusters

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Evolutionary fuzzy clustering algorithm with knowledge-based evaluation and applications for gene expression profiling

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JOURNAL OF COMPUTATIONAL AND THEORETICAL NANOSCIENCE 2005年第4期2卷 524-533页

作者： Park, HS Yoo, SH Cho, SB Yonsei Univ Dept Comp Sci Seoul 120749 South Korea

In microarray data analysis, clustering is a method that groups thousands of genes by their similarities of expression levels, helping to analyze gene expression profiles. This method has been used for identifying unknown functions of genes. The fuzzy clustering method assigns one sample to multiple groups according to their degrees of membership. This method is more appropriate for analyzing gene expression profiles, because a single gene might be involved in multiple functions. General clustering methods, however, have problems in that they are sensitive to initialization and can be trapped into local optima. To overcome these problems, we propose an evolutionary fuzzy clustering method with knowledge-based evaluation. The proposed method uses a genetic algorithm for clustering and prior knowledge of experimental data for evaluation. We have performed experiments to show the usefulness of the proposed method with yeast cell-cycle and SRBCT datasets.

关键词： evolutionary fuzzy clustering knowledge-based evaluation gene expression profiles microarray data analysis

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An Iterative Locally Auto-Weighted Least Squares Method for microarray Missing Value Estimation

IEEE TRANSACTIONS ON NANOBIOSCIENCE

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IEEE TRANSACTIONS ON NANOBIOSCIENCE 2017年第1期16卷 21-33页

作者： Yu, Zeng Li, Tianrui Horng, Shi-Jinn Pan, Yi Wang, Hongjun Jing, Yunge Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 611756 Peoples R China Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA Natl Taiwan Univ Sci & Technol Dept Comp Sci & Informat Engn Taipei 106 Taiwan

microarray data often contain missing values which significantly affect subsequent analysis. Existing LLSimpute-based imputation methods for dealing with missing data have been shown to be generally efficient. However, all of the LLSimpute-based methods do not consider the different importance of different neighbors of the target gene in the missing value estimation process and treat all the neighbors equally. In this paper, a locally auto-weighted least squares imputation (LAW-LSimpute) method is proposed for missing value estimation, which can automatically weight the neighboring genes based on the importance of the genes. Then, an accelerating strategy is added to the LAW-LSimpute method in order to improve the convergence. Furthermore, an iterative missing value estimation framework of LAW-LSimpute (ILAW-LSimpute) is designed. Experimental results show that the ILAW-LSimpute method is able to reduce the estimation error.

关键词： Auto-weighted local least squares iterative estimation microarray data analysis missing value estimation

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