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IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)

作者： Xie, Ruifei Han, Bin Li, Lihua Zhang, Juan Zhu, Lei Hangzhou Dianzi Univ Coll Life Informat Sci & Instrument Engn Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781457716133

Extracting significant features from high-dimensional and small sample-size microarray data is a challenging problem. Other than wrapper or filter methods, we propose a novel feature selection algorithm which integrates the ideas of professional tennis players ranking, such as seed players and dynamic ranking with Monte Carlo simulation. Seed players make the 'game' more competitive and selective, hence improve the selection efficiency. Besides, the ranks of features are dynamically updated and this ensures that it is always the current best players to take part in each competitions. The proposed algorithm is tested on widely used public datasets. Results demonstrate that the proposed method comparatively converges faster, more stable and has good performance in classification and therefore is an efficient algorithm for feature selection.

关键词： Algorithm design and analysis Classification algorithms Equations Games Heuristic algorithms Monte Carlo feature selection Monte Carlo methods Monte Carlo simulation Testing Training data handling microarray data professional tennis player ranking strateg

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An evaluation of microarray visualization tools for biological insight

An evaluation of microarray visualization tools for biologic...

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10th Annual IEEE Symposium on Information Visualization (InfoVis 2004)

作者： Saraiya, P North, C Duca, K Virginia Polytech Inst & State Univ Dept Comp Sci Blacksburg VA 24061 USA

ISBN: (纸本)0780387791

High-throughput experiments such as gene expression microarrays in the life sciences result in large datasets. In response, a wide variety of visualization tools have been created to facilitate data analysis. Biologists often face a dilemma in choosing the best tool for their situation. The tool that works best for one biologist may not work well for another due to differences in the type of insight they seek from their data. A primary purpose of a visualization tool is to provide domain-relevant insight into the data. Ideally, any user wants maximum information in the least possible time. In this paper we identify several distinct characteristics of insight that enable us to recognize and quantify it. Based on this, we empirically evaluate five popular microarray visualization tools. Our conclusions can guide biologists in selecting the best tool for their data, and computer scientists in developing and evaluating visualizations.

关键词： data visualization empirical evaluation insight high throughput experiments microarray data bioinformatics

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Ensemble Approach for Predicting Genetic Disease through Case-Control Study 2

Ensemble Approach for Predicting Genetic Disease through Cas...

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International Conference on Inventive Communication and Computational Technologies (ICICCT)

作者： Francis, Frangly Namitha, T. N. Jyothi Engn Coll Dept Comp Sci & Engn Trichur India

ISBN: (纸本)9781538619742

The origin of many diseases can be revealed by knowing the gene regulation. Genetic disease an abnormality in individual's genome. These abnormalities can be occurred due to mutation in single base in DNA or due to the involvement in the addition or subtraction of entire chromosomes. To measure the expression levels of bulk of genes simultaneously, DNA microarrays are used. microarray data is set of DNA sequences representing the entire set of genes of an organism, arranged in a grid pattern so that the expression of thousands of genes can be detected at the same time. In this paper we propose an approach for diagnosing genetic disease, to accomplish this goal we need to select the most informative ones that can accurately distinguish one class of genes from another. For that the genes with high significance are iteratively extracted. The goal of this paper is to develop method, which increases the classification accuracy, for identifying significant differentially expressed clusters of genes using Recursive Cluster Elimination (RCE) that is based on an ensemble clustering approach.

关键词： genetic disease microarray data clustering bioinformatics genomics machine learning DNA support vector machine recursive cluster elimination gene selection

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A New F-Test Applicable to Large-Scale data

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Journal of Statistical Theory and Applications 2019年第4期18卷 439-449页

作者： Salehi, Mohsen Mohammadpour, Adel Mengersen, Kerrie Department of Statistics University of Qom Qom Iran Department of Statistics Amirkabir University of Technology (Tehran Polytechnic) Tehran Iran Science and Engineering Faculty Queensland University of Technology Brisbane Australia

In large-scale multiple testing, the permutation test based on making a null statistic has been widely employed in the literature. Because it enables us to use the null permuted samples and estimate the p-value more accurately. Some test statistics, which can be modified to a null statistic, have been proposed for two independent groups. In this paper, we propose a new statistic and corresponding F-test, which can be applied for three or more groups. The simulation results demonstrate that the proposed method has a smaller interquartile range (IQR) than the usual permutation method and estimates p-values more accurately than its rival. © 2019, The Authors. Published by Atlantis Press SARL.

关键词： F-test microarray data Null statistic Permutation test

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Machine Learning Algorithms for Predicting Chronic Obstructive Pulmonary Disease from Gene Expression data with Class Imbalance 12

Machine Learning Algorithms for Predicting Chronic Obstructi...

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14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC) / 12th International Conference on Bioinformatics Models, Methods and Algorithms (BIOINFORMATICS)

作者： Mahmudah, Kunti Robiatul Purnama, Bedy Indriani, Fatma Satou, Kenji Kanazawa Univ Grad Sch Nat Sci & Technol Kanazawa Ishikawa Japan TELKOM Univ Telkom Sch Comp Bandung Indonesia Univ Lambung Mangkurat Dept Comp Sci Banjarbaru Indonesia Kanazawa Univ Inst Sci & Engn Kanazawa Ishikawa Japan

ISBN: (纸本)9789897584909

Chronic obstructive pulmonary disease (COPD) is a progressive inflammatory lung disease that causes breathlessness and leads to serious illness including lung cancer. It is estimated that COPD caused 5% of all deaths globally in 2015, putting COPD as the three leading causes of death worldwide. This study proposes methods that utilize gene expression data from microarrays to predict the presence or absence of COPD. The proposed method assists in determining better treatments to lower the fatality rates. In this study, microarray data of the small airway epithelium cells obtained from 135 samples of 23 smokers with COPD (9 GOLD stage I, 12 GOLD stage II, and 2 GOLD stage III), 59 healthy smokers, and 53 healthy nonsmokers were selected from GEO dataset. Machine learning and regression algorithms performed in this study included Random Forest, Support Vector Machine, Naive Bayes, Gradient Boosting Machines, Elastic Net Regression, and Multiclass Logistic Regression. After diminishing imbalance data effect using SMOTE, classification algorithms were performed using 825 of the selected features. High AUC score was achieved by elastic net regression and multiclass logistic regression with AUC of 89% and 90%, respectively. In the metrics including accuracy, specificity, and sensitivity, both classifiers also outperformed the others.

关键词： microarray data Gene Expression COPD Machine Learning Class Imbalance

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Binary Classification Based on Potential Functions

Binary Classification Based on Potential Functions

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4th Annual Ohio Collaborative Conference on Bioinformatics

作者： Boczko, Erik M. Di Lullo, Andrew Young, Todd R. Vanderbilt Univ Med Ctr Nashville TN 37235 USA Ohio Univ Dept Phys Athens OH 45701 USA Ohio Univ Dept Math Athens OH 45701 USA

We introduce a simple and computationally trivial method for binary classification based on the evaluation of potential functions. We demonstrate that despite the conceptual and computational simplicity of the method ... 详细信息

ISBN: (纸本)9780769536859

关键词： Machine Learning microarray data

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A hybrid genetic algorithm and expectation maximization method for global gene trajectory clustering

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Journal of Bioinformatics and Computational Biology 2005年第5期3卷 1227-1242页

作者： Chan, Zeke S.H. Kasabov, N. Collins, Lesley Knowledge Engineering and Discovery Research Institute Auckland University of Technology Auckland New Zealand Allan Wilson Centre for Molecular Ecology and Evolution Massey University Palmerston North New Zealand

Clustering time-course gene expression data (gene trajectories) is an important step towards solving the complex problem of gene regulatory network modeling and discovery as it significantly reduces the dimensionality of the gene space required for analysis. Traditional clustering methods that perform hill-climbing from randomly initialized cluster centers are prone to produce inconsistent and sub-optimal cluster solutions over different runs. This paper introduces a novel method that hybridizes genetic algorithm (GA) and expectation maximization algorithms (EM) for clustering gene trajectories with the mixtures of multiple linear regression models (MLRs), with the objective of improving the global optimality and consistency of the clustering performance. The proposed method is applied to cluster the human fibroblasts and the yeast time-course gene expression data based on their trajectory similarities. It outperforms the standard EM method significantly in terms of both clustering accuracy and consistency. The biological implications of the improved clustering performance are demonstrated. © Imperial College Press.

关键词： Expectation maximization Gene regulatory networks Gene trajectory clustering Genetic algorithm microarray data

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microarray Classification of Cancerous Cell Using Soft Computing Technique 4th

Microarray Classification of Cancerous Cell Using Soft Compu...

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4th International Conference on Advances in Computing, Communication and Control (ICAC3)

作者： Vijayeeta, Prachi Kar, Utsav Rana, Madhurima Das, Madhabananda Mishra, B. S. P. KIIT Univ Sch Comp Engn Odisha India

In the late 19th century, the advent of malignant tissues in the human cells has come into limelight. A lot of research works has been carried out to detect the tumorous cells and subsequently, various methodologies have been adopted to distinguish different types of cancerous cells. Gene expression based microarray technique has been used as an efficient and emerging technique in the field of cancer classification, prediction, diagnosis and many other type of research. In this paper,we have used microarray data which is a collection of microscopic spots. Since there is an existence of huge dataset, the dimensionality reduction of the dataset is done at the first stage of our work. At the second stage, classification is performed using supervised learning methods. At the final stage, we have optimized the objective function using soft computing technique which yields the relevant *** computing refers to a consortium of computational methodologies that has motivated many scientific researchers to contribute their efforts in designing highly powerful intelligent systems. We are mainly focusing on the classification of cancerous cells using two classifiers called SVM (support vector machine) and K-NN (K-nearest neighbor) and an optimization technique is being incorporated on significant parameters that results in better and relevant output. (C) 2015 Published by Elsevier B.V. This is an open access article under CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： microarray data Support Vector Machine (SVM) K-NN(K- Nearest Neighbor) Soft computing

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Time Series Gene Expression data Classification via L₁-norm Temporal SVM

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5th International Conference on Pattern Recognition in Bioinformatics

作者： Orsenigo, Carlotta Vercellis, Carlo Politecn Milan Dept Management Econ & Ind Engn I-20156 Milan Italy

ISBN: (纸本)9783642160004

Machine learning methods have been successfully applied to the phenotype classification of many diseases based on static gene expression measurements. More recently microarray data have been collected over time, making available datasets composed by time series of expression gene profiles. In this paper we propose a new method for time series classification, based on a temporal extension of L-1-norm support vector machines, that uses dynamic time warping distance for measuring time series similarity. This results in a mixed-integer optimization model which is solved by a sequential approximation algorithm. Computational tests performed on two benchmark datasets indicate the effectiveness of the proposed method compared to other techniques, and the general usefulness of the approaches based on dynamic time warping for labeling time series gene expression data.

关键词： Time series classification microarray data L-1-norm support vector machines dynamic time warping

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A new approach: Interrelated two-way clustering of gene expression data

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Statistical Methodology 2006年第1期3卷 93-102页

作者： Chandra, B. Shanker, S. Mishra, Saroj Department of Mathematics Indian Institute of Technology Delhi India Department of Biochemical Engineering and Biotechnology Indian Institute of Technology Delhi India

The paper presents a new approach to interrelated two-way clustering of gene expression data. Clustering of genes has been effected using entropy and a correlation measure, whereas the samples have been clustered using the fuzzy C-means. The efficiency of this approach has been tested on two well known data sets: the colon cancer data set and the leukemia data set. Using this approach, we were able to identify the important co-regulated genes and group the samples efficiently at the same time. © 2005 Elsevier B.V. All rights reserved.

关键词： Gene expression data microarray data Two-way clustering

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