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Rank-invariant resampling based estimation of false discovery rate for analysis of small sample microarray data

BMC BIOINFORMATICS 2005年第1期6卷 1-9页

作者： Jain, N Cho, HJ O'Connell, M Lee, JK Univ Virginia Sch Med Dept Hlth Evaluat Sci Div Biostat & Epidemiol Charlottesville VA 22908 USA Insightful Corp Durham NC 27713 USA

Background: The evaluation of statistical significance has become a critical process in identifying differentially expressed genes in microarray studies. Classical p-value adjustment methods for multiple comparisons such as family-wise error rate (FWER) have been found to be too conservative in analyzing large-screening microarray data, and the False Discovery Rate (FDR), the expected proportion of false positives among all positives, has been recently suggested as an alternative for controlling false positives. Several statistical approaches have been used to estimate and control FDR, but these may not provide reliable FDR estimation when applied to microarray data sets with a small number of replicates. Results: We propose a rank-invariant resampling (RIR) based approach to FDR evaluation. Our proposed method generates a biologically relevant null distribution, which maintains similar variability to observed microarray data. We compare the performance of our RIR-based FDR estimation with that of four other popular methods. Our approach outperforms the other methods both in simulated and real microarray data. Conclusion: We found that the SAM's random shuffling and SPLOSH approaches were liberal and the other two theoretical methods were too conservative while our RIR approach provided more accurate FDR estimation than the other approaches.

关键词： False Discovery Rate microarray data Null Distribution microarray data Analysis Null data

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A Modified Firefly Deep Ensemble for microarray data Classification

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COMPUTER JOURNAL 2022年第12期65卷 3265-3274页

作者： Vijay, Arul Antran S. Prakash, Jothi, V Karpagam Coll Engn Dept Comp Sci & Engn Coimbatore Tamil Nadu India Karpagam Coll Engn Dept Informat Technol Coimbatore Tamil Nadu India

Many researchers are using microarray technology to examine and investigate the levels of gene expression in a specific organism, which is an emerging trend in the field of genetic research. microarray studies have a wide range of applications in the health sector, including disease prediction and diagnostics, as well as cancer research. Due to the existence of irrelevant or duplicated data in microarray datasets, it is difficult to correctly and immediately capture possible patterns using existing algorithms. Feature selection (FS) has evolved into a critical approach for identifying and eliminating the most pertinent qualities. The enormous dimensionality of microarray datasets, on the other hand, presents a significant barrier to the majority of available FS techniques. In this research, we propose a Modified Firefly Feature Selection (MFFS) algorithm that will reduce the irrelevant attributes needed for classification and a Deep Learning Model for classifying the microarray data. The experimental outcomes show that the proposed MFFS algorithm combined with a Hybrid Deep Learning Algorithm outperforms the existing methods in terms of feature set size, accuracy, precision, recall, F-measure and AUC for a dataset with larger number of features.

关键词： deep learning ensemble technique firefly algorithm feature selection microarray data

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Dimension reduction-based penalized logistic regression for cancer classification using microarray data

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2005年第2期2卷 166-175页

作者： Shen, L Tan, EC Nanyang Technol Univ BioInformat Res Ctr Singapore 637553 Singapore Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore

The use of penalized logistic regression for cancer classification using microarray expression data is presented. Two dimension reduction methods are respectively combined with the penalized logistic regression so that both the classification accuracy and computational speed are enhanced. Two other machine-learning methods, support vector machines and least-squares regression, have been chosen for comparison. It is shown that our methods have achieved at least equal or better results. They also have the advantage that the output probability can be explicitly given and the regression coefficients are easier to interpret. Several other aspects, such as the selection of penalty parameters and components, pertinent to the application of our methods for cancer classification are also discussed.

关键词： dimension reduction penalized logistic regression singular value decomposition partial least squares cancer classification classifier design and evaluation feature evaluation and selection microarray data

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An effective double-bounded tree-connected Isomap algorithm for microarray data classification

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PATTERN RECOGNITION LETTERS 2012年第1期33卷 9-16页

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

Isometric mapping (Isomap) is a popular nonlinear dimensionality reduction technique which has shown high potential in visualization and classification. However, it appears sensitive to noise or scarcity of observations. This inadequacy may hinder its application for the classification of microarray data, in which the expression levels of thousands of genes in a few normal and tumor sample tissues are measured. In this paper we propose a double-bounded tree-connected variant of Isomap, aimed at being more robust to noise and outliers when used for classification and also computationally more efficient. It differs from the original Isomap in the way the neighborhood graph is generated: in the first stage we apply a double-bounding rule that confines the search to at most k nearest neighbors contained within an epsilon-radius hypersphere;the resulting subgraphs are then joined by computing a minimum spanning tree among the connected components. We therefore achieve a connected graph without unnaturally inflating the values of k and epsilon. The computational experiences show that the new method performs significantly better in terms of accuracy with respect to Isomap, k-edge-connected Isomap and the direct application of support vector machines to data in the input space, consistently across seven microarray datasets considered in our tests. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Nonlinear dimensionality reduction Isomap microarray data Support vector machines

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Boosting and microarray data

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MACHINE LEARNING 2003年第1-2期52卷 31-44页

作者： Long, PM Vega, VB Genome Inst Singapore Singapore Singapore

We have found one reason why AdaBoost tends not to perform well on gene expression data, and identified simple modifications that improve its ability to find accurate class prediction rules. These modifications appear especially to be needed when there is a strong association between expression profiles and class designations. Cross-validation analysis of six microarray datasets with different characteristics suggests that, suitably modified, boosting provides competitive classification accuracy in general. Sometimes the goal in a microarray analysis is to find a class prediction rule that is not only accurate, but that depends on the level of expression of few genes. Because boosting makes an effort to find genes that are complementary sources of evidence of the correct classification of a tissue sample, it appears especially useful for such gene-efficient class prediction. This appears particularly to be true when there is a strong association between expression profiles and class designations, which is often the case for example when comparing tumor and normal samples.

关键词： supervised learning classification boosting gene expression data microarray data bioinformatics

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Random walk biclustering for microarray data

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INFORMATION SCIENCES 2008年第6期178卷 1479-1497页

作者： Angiulli, Fabrizio Cesario, Eugenio Pizzuti, Clara CNR ICAR I-87036 Arcavacata Di Rende CS Italy

A biclustering algorithm, based on a greedy technique and enriched with a local search strategy to escape poor local minima, is proposed. The algorithm starts with an initial random solution and searches for a locally optimal solution by successive transformations that improve a gain function. The gain function combines the mean squared residue, the row variance, and the size of the bicluster. Different strategies to escape local minima are introduced and compared. Experimental results on several microarray data sets show that the method is able to find significant biclusters, also from a biological point of view. (c) 2007 Elsevier Inc. All rights reserved.

关键词： biclustering microarray data local search

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Expectation Propagation for microarray data classification

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PATTERN RECOGNITION LETTERS 2010年第12期31卷 1618-1626页

作者： Hernandez-Lobato, Daniel Hernandez-Lobato, Jose Miguel Suarez, Alberto Catholic Univ Louvain Dept Comp Sci & Engn B-1348 Louvain Belgium Univ Autonoma Madrid Escuela Politecn Super E-28049 Madrid Spain

microarray experiments are a very promising tool for early diagnosis and disease treatment. The datasets obtained in these experiments typically consist of a small number of instances and a large number of covariates, most of which are irrelevant for discrimination. These characteristics pose severe difficulties for standard learning algorithms. A Bayesian approach can be useful to overcome these problems and produce more accurate and robust predictions. However, exact Bayesian inference is computationally costly and in many cases infeasible. In practice, some form of approximation has to be made. In this paper we consider a Bayesian linear model for microarray data classification based on a prior distribution that favors sparsity in the model coefficients. Expectation Propagation (EP) is then used to perform approximate inference as an alternative to computationally more expensive methods, such as Markov Chain Monte Carlo (MCMC) sampling. The model considered is evaluated on 15 microarray datasets and compared with other state-of-the-art classification algorithms. These experiments show that the Bayesian model trained with EP performs well on the datasets investigated and is also useful to identify relevant genes for subsequent analysis. (C) 2010 Elsevier B.V. All rights reserved.

关键词： microarray data Bayesian inference Expectation Propagation

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Pipelining the ranking techniques for microarray data classification: A case study

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APPLIED SOFT COMPUTING 2016年第0期48卷 298-316页

作者： Dash, Rasmita Misra, Bijan Bihari Siksha O Anusandhan Univ Inst Tech Educ & Res Dept Comp Sc & Informat Technol Bhubaneswar 751030 Odisha India Silicon Inst Technol Dept Comp Sc & Engn Bhubaneswar 751024 Odisha India

Identification of relevant genes from microarray data is an apparent need in many applications. For such identification different ranking techniques with different evaluation criterion are used, which usually assign different ranks to the same gene. As a result, different techniques identify different gene subsets, which may not be the set of significant genes. To overcome such problems, in this study pipelining the ranking techniques is suggested. In each stage of pipeline, few of the lower ranked features are eliminated and at the end a relatively good subset of feature is preserved. However, the order in which the ranking techniques are used in the pipeline is important to ensure that the significant genes are preserved in the final subset. For this experimental study, twenty four unique pipeline models are generated out of four gene ranking strategies. These pipelines are tested with seven different microarray databases to find the suitable pipeline for such task. Further the gene subset obtained is tested with four classifiers and four performance metrics are evaluated. No single pipeline dominates other pipelines in performance;therefore a grading system is applied to the results of these pipelines to find out a consistent model. The finding of grading system that a pipeline model is significant is also established by Nemenyi post-hoc hypothetical test. Performance of this pipeline model is compared with four ranking techniques, though its performance is not superior always but majority of time it yields better results and can be suggested as a consistent model. However it requires more computational time in comparison to single ranking techniques. (C) 2016 Elsevier B.V. All rights reserved.

关键词： microarray data Feature selection Feature ranking technique Classification Statistical test

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TMA-TAB: A spreadsheet-based document for exchange of tissue microarray data based on the tissue microarray-object model

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JOURNAL OF BIOMEDICAL INFORMATICS 2010年第3期43卷 435-441页

作者： Song, Young Soo Lee, Hye Won Park, Yu Rang Kim, Do Kyoon Sim, Jaehyun Kang, Hyunseok Peter Kim, Ju Han Seoul Natl Univ Coll Med Div Biomed Informat Seoul 110799 South Korea Seoul Natl Univ Coll Med SNUBI Seoul 110799 South Korea Univ Florida Coll Med Dept Mol Genet & Microbiol Gainesville FL 32611 USA Roswell Pk Canc Inst Dept Pathol & Lab Med Buffalo NY 14263 USA

The importance of tissue microarrays (TMA) as clinical validation tools for cDNA microarray results is increasing, whereas researchers are still suffering from TMA data management issues. After we developed a comprehensive data model for TMA data storage, exchange and analysis, TMA-OM, we focused our attention on the development of a user-friendly exchange format with high expressivity in order to promote data communication of TMA results and TMA-OM supportive database applications. We developed TMA-TAB, a spreadsheet-based data format for TMA data submission to the TMA-OM supportive TMA database system. TMA-TAB was developed by simplifying, modifying and reorganizing classes, attributes and templates of TMA-OM into five entities: experiment, block, slide, core_in_block, and core_in_slide. Five tab-delimited formats (investigation design format, block description format, slide description format, core clinicohistopathological data format, and core result data format) were made, each representing the entities of experiment, block, slide, core_in_block, and core_in_slide. We implemented TMA-TAB import and export modules on Xperanto-TMA, a TMA-OM supportive database application, to facilitate data submission. Development and implementation of TMA-TAB and TMA-OM provide a strong infrastructure for powerful and user-friendly TMA data management. (C) 2009 Elsevier Inc. All rights reserved.

关键词： database microarray data Modeling Tissue microarray

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Two-stage classification methods for microarray data

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EXPERT SYSTEMS WITH APPLICATIONS 2008年第1期34卷 375-383页

作者： Wong, Tzu-Tsung Hsu, Ching-Han Natl Cheng Kung Univ Inst Informat Management Tainan 701 Taiwan

Gene expression data are a key factor for the success of medical diagnosis, and two-stage classification methods are therefore developed for processing microarray data. The first stage for this kind of classification methods is to select a pre-specified number of genes, which are likely to be the most relevant to the occurrence of a disease, and passes these genes to the second stage for classification. In this paper, we use four gene selection mechanisms and two classification tools to compose eight two-stage classification methods, and test these eight methods on eight microarray data sets for analyzing their performance. The first interesting finding is that the genes chosen by different categories of gene selection mechanisms are less than half in common but result in insignificantly different classification accuracies. A subset-gene-ranking mechanism can be beneficial in classification accuracy, but its computational effort is much heavier. Whether the classification tool employed at the second stage should be accompanied with a dimension reduction technique depends on the characteristics of a data set. (c) 2006 Elsevier Ltd. All rights reserved.

关键词： dimension reduction gene selection microarray data two-stage classification method

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