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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Cross, Valerie Zmuda, Michael Paul, Rahul Hall, Lawrence Miami Univ Comp Sci & Software Engn Oxford OH 45056 USA Univ S Florida Comp Sci & Engn Tampa FL 33620 USA

ISBN: (纸本)9781728169323

A problem for machine learning research occurs when many possible features exist but the training data examples are very few. For example, microarray data typically have a much larger number of features, the genes, as compared to the number of training data examples, the patients. One approach is to first determine the best features for prediction and then to group features based on a measure of their relatedness. The concordance correlation coefficient has been used to place somewhat correlated features into disjoint groups of similar features. Multiple base classifiers are created by randomly picking one feature from each of the feature groups and then the collection of base classifiers is used in an ensemble classifier. Each classifier in the ensemble provides a vote. The majority vote is used to produce the final class prediction. This paper investigates grouping features using fuzzy set similarity measures as well as the concordance correlation coefficient as a relatedness measure. The performance of these different measures is compared in terms of accuracy, sensitivity, specificity, and F-measure using the ensemble classifiers created with the different relatedness measures. Four microarray gene expression data sets are used in the experiments to determine the usefulness of fuzzy set similarity measures and how they compare with the concordance correlation coefficient. Using the concordance correlation coefficient to guide clustering is not superior to fuzzy set similarity measures. Depending on the particular data set and performance measure being used, different fuzzy , set similarity measures perform better than or just as well as the concordance correlation coefficient.

关键词： feature selection fuzzy set similarity measures concordance correlation coefficient microarray data

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microarray cancer feature selection: Review, challenges and research directions

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International Journal of Cognitive Computing in Engineering 2020年 1卷 78-97页

作者： Hambali, Moshood A. Oladele, Tinuke O. Adewole, Kayode S. Department of Computer Science Federal University Wukari Taraba Nigeria Department of Computer Science University of Ilorin Kwara Nigeria

microarray technology has become an emerging trend in the domain of genetic research in which many researchers employ to study and investigate the levels of genes’ expression in a given organism. microarray experiments have lots of application areas in the health sector such as diseases prediction and diagnosis, cancer study and soon. The enormous quantity of raw gene expression data usually results in analytical and computational complexities which include feature selection and classification of the datasets into the correct class or group. To achieve satisfactory cancer classification accuracy with the complete set of genes remains a great challenge, due to the high dimensions, small sample size, and presence of noise in gene expression data. Feature reduction is critical and sensitive in the classification task. Therefore, this paper presents a comprehensive survey of studies on microarray cancer classification with a focus on feature selection methods. In this paper, the taxonomy of the various feature selection methods used for microarray cancer classification and open research issues have been extensively discussed. © 2020 The Authors

关键词： Embedded Feature selection Filter microarray data microarray technology Wrapper

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An Iterated Conditional Modes Solution for Sparse Bayesian Factor Modeling of Transcriptional Regulatory Networks

An Iterated Conditional Modes Solution for Sparse Bayesian F...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Meng, Jia Zhang, Jianqiu Chen, Yidong Huang, Yufei Univ Texas San Antonio Dept Elect & Comp Engn San Antonio TX 78249 USA UT Hlth Sci Ctr San Antonio Dept Epidemiol & Biostat San Antonio TX USA UT Hlth Sci Ctr San Antonio Greehey Childrens Canc Res Inst San Antonio TX USA

ISBN: (纸本)9781424483075

The problem of uncovering transcriptional regulation by transcription factors (TFs) based on microarray data is considered. A novel Bayesian sparse correlated rectified factor model (BSCRFM) coupled with its ICM solution is proposed. BSCRFM models the unknown TF protein level activity, the correlated regulations between TFs, and the sparse nature of TF regulated genes and it admits prior knowledge from existing database regarding TF regulated target genes. An efficient Iterated Conditional Modes (ICM) algorithm is developed, and a maximum a posterior (MAP) solution is calculated from multiple ICM results to avoid the local maximum problem, a context-specific transcriptional regulatory network specific to the experimental condition of the microarray data can then be obtained. The proposed model's ICM algorithm and MAP solution are evaluated on the simulated systems and results demonstrated the validity and effectiveness of the proposed approach. The proposed model is also applied to the breast cancer microarray data and a TF regulated network is obtained.

关键词： BSCRFM model Bayes methods Bayesian sparse correlated rectified factor model bioinformatics cellular biophysics iterated conditional modes maximum a posterior solution microarray data protein proteins transcriptional regulatory network

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Model-free Bias Reduction of Storey’s Estimator for the Proportion of True Null Hypotheses

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Calcutta Statistical Association Bulletin 2022年第1期74卷 27-41页

作者： Biswas, Aniket Department of Statistics Dibrugarh University Assam Dibrugarh India

Storey’s bootstrap estimator for the proportion of true null hypotheses (π0) has a known bias structure but the amount of bias is obviously unknown. A new method has been proposed in this work to reduce bias of the ... 详细信息

Storey’s bootstrap estimator for the proportion of true null hypotheses (π₀) has a known bias structure but the amount of bias is obviously unknown. A new method has been proposed in this work to reduce bias of the estimator. The proposed method partitions differential gene expression and similar data sets randomly and repeatedly to first approximate the bias and then corrects it in the final estimate. Unlike some recent developments, the method does not require any model assumption. The proposed method depends on some tuning parameters. For practical applicability of the method, ideal choices of the tuning parameters are given based on numerical results. Extensive simulation experiments show favourable results for the proposed method over the popular robust estimators in the literature. Two real-life microarray data sets have been studied to demonstrate the applicability of the final bias and variance reduced estimator. AMS 2010 subject classifications: 62F10, 62F40 © 2022 Calcutta Statistical Association, Kolkata.

关键词： microarray data p-value Repeated random partitioning simultaneous testing t -test upper tail probability

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Internal Evaluation Measures as Proxies for External Indices in Clustering Gene Expression data

Internal Evaluation Measures as Proxies for External Indices...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Vukicevic, Milan Delibasic, Boris Jovanovic, Milos Suknovic, Milija Obradovic, Zoran Univ Belgrade Fac Org Sci Ctr Business Decis Making Belgrade Serbia Temple Univ Ctr Data Analyt & Biomed Informat Philadelphia PA 19122 USA

ISBN: (纸本)9780769545745

Several external indices that use information not present in the dataset were shown to be useful for evaluation of representative based clustering algorithms. However, such supervised measures are not directly useful for construction of better clustering algorithms when class labels are not provided. We propose a method for identifying internal cluster evaluation measures that use only information present in the dataset and are related to given external indices. We utilize these internal measures for the construction of representative based clustering algorithms. Both identification and utilization steps of the proposed method are enabled by use of a component-based clustering algorithm design. Experiments on 432 algorithms using gene expression data sets provide evidence that some internal measures could be used as surrogates for external indices proposed in the literature. Moreover, the obtained results suggest that internal measures correlated to selected external indices can guide the algorithms toward significantly better cluster models.

关键词： component-based clustering evaluation measures microarray data

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Gene Selection Using Interaction Information for microarray-based Cancer Classification 13

Gene Selection Using Interaction Information for Microarray-...

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13th IEEE Annual Conference on Computational Intelligence in Bioinformatics and Computational Biology (IEEE CIBCB)

作者： Nakariyakul, Songyot Thammasat Univ Dept Elect & Comp Engn Khlong Luang 12120 Pathumthani Thailand

ISBN: (纸本)9781467394727

Gene selection is an important pre-processing step in microarray analysis and classification. While traditional gene selection algorithms focus on identifying relevant and irredundant genes, we present a new gene selection algorithm that chooses gene subsets based on their interaction information. Many individual genes may be irrelevant with the class, but when combined together, they can interact and provide information useful for classification. Our proposed gene selection algorithm is tested on four well-known cancer microarray datasets. Initial results show that our algorithm selects effective gene subsets and outperforms prior gene selection algorithm in terms of classification accuracy.

关键词： cancer classification feature selection gene selection interaction information microarray data wrapper method

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Enhanced Cancer Subtyping via Pan-Transcriptomics data Fusion, Monte-Carlo Consensus Clustering, and Auto Classifier Creation 10

Enhanced Cancer Subtyping via Pan-Transcriptomics Data Fusio...

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10th International Conference on Computational Systems-Biology and Bioinformatics (CSBio)

作者： Linton-Reid, Kristofer Clifford, Harry Thompson, Joe Sneath Imperial Coll London London England Cambridge Canc Genom Cambridge England

ISBN: (纸本)9781450372152

Subtyping of tumor transcriptome expression profiles is a routine method used to distinguish tumor heterogeneity. Unsupervised clustering techniques are often combined with survival analysis to decipher the relationship between genes and the survival times of patients. However, the reproducibility of these subtyping based studies is poor. There are multiple reports which have conflicting subtype and gene-survival time relationship results. In this study, we introduce the issues underlying the lack of reproducibility in transcriptomic subtyping studies. This problem arises from the routine analysis of small cohorts (< 100 individuals) and use of biased traditional consensus clustering techniques. Our approach carefully combines multiple RNA-sequencing and microarray datasets, followed by subtyping via Monte-Carlo Consensus Clustering and creation of deep subtyping classifiers. This paper demonstrates an improved subtyping methodology by investigating pancreatic ductal adenocarcinoma. Importantly, our methodology identifies six biologically novel pancreatic ductal adenocarcinoma subtypes. Our approach also enables a degree of reproducibility, via our pancreatic ductal adenocarcinoma classifier PDACNet, which classical subtyping studies have failed to establish.

关键词： Consensus Clustering Cancer Subtyping Gene Expression Pancreatic Cancer RNA-Sequencing data microarray data

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A multivariate extension of the gene set enrichment analysis

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Journal of Bioinformatics and Computational Biology 2007年第5期5卷 1139-1153页

作者： Klebanov, Lev Glazko, Galina Salzman, Peter Yakovlev, Andrei Xiao, Yuanhui Department of Probability and Statistics Charles University Praha-8 CZ-18675 Sokolovska 83 Czech Republic Department of Biostatistics and Computational Biology University of Rochester Rochester NY 14642 601 Elmwood Avenue United States Department of Mathematics and Statistics Georgia State University Atlanta GA 30303 United States

A test-statistic typically employed in the gene set enrichment analysis (GSEA) prevents this method from being genuinely multivariate. In particular, this statistic is insensitive to changes in the correlation structure of the gene sets of interest. The present paper considers the utility of an alternative test-statistic in designing the confirmatory component of the GSEA. This statistic is based on a pertinent distance between joint distributions of expression levels of genes included in the set of interest. The null distribution of the proposed test-statistic, known as the multivariate N-statistic, is obtained by permuting group labels. Our simulation studies and analysis of biological data confirm the conjecture that the N-statistic is a much better choice for multivariate significance testing within the framework of the GSEA. We also discuss some other aspects of the GSEA paradigm and suggest new avenues for future research. © 2007 Imperial College Press.

关键词： Gene sets microarray data Multivariate testing Probability distance

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A THREE-STAGE METHOD TO SELECT INFORMATIVE GENES FOR CANCER CLASSIFICATION

A THREE-STAGE METHOD TO SELECT INFORMATIVE GENES FOR CANCER ...

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40th ISCIE International Symposium on Stochastic Systems Theory and Its Applications

作者： Mohamad, Mohd Saberi Omatu, Sigeru Yoshioka, Michifumi Deris, Safaai Osaka Prefecture Univ Grad Sch Engn Dept Comp Sci & Intelligent Syst Osaka 5998531 Japan Univ Teknol Malaysia Dept Software Engn Fac Comp Sci & Informat Syst Skudai 81310 Johore Malaysia

microarray technology has provided biologists with the ability to measure the expression levels of thousands of genes in a single experiment. One of the urgent issues in the use of microarray data is the selection of a small subset of genes from the thousands of genes in the data that contributes to a disease. This selection process is difficult due to many irrelevant genes, noisy genes, and the availability of the small number of Samples compared to the huge number Of genes (high-dimensional data). In this study, we propose a three-stage gene selection method to select a small subset of informative genes that is most relevant for the cancer classification. It has three stages: 1) pre-selecting genes using a filter method to produce a subset of genes;2) optimising the gene Subset using a multi-objective hybrid method to yield near-optimal gene Subsets;3) analysing the frequency of appearance of each gene in the different near-optimal gene subsets to produce a small subset of informative genes. The experimental results show that our proposed method is capable in selecting the small subset to obtain better classification accuracies than other related previous works as well as five methods experimented in this work. Additionally, a list of informative genes in the final gene subsets is also presented for biological usage.

关键词： A three-stage method Gene selection Hybrid approach microarray data

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Finding Marker Genes from High Dimensional Expression Profiles: Divide-and-conquer Exploiting a Fuzzy Rule Based Framework

Finding Marker Genes from High Dimensional Expression Profil...

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IEEE International Conference on Fuzzy Systems (FUZZ)

作者： Huang, Sheng-Yao Chen, Yi-Cheng Chung, I-Fang Yang, Feng-Yi Su, Chun-Hung Natl Yang Ming Univ Inst Biomed Informat Taipei 112 Taiwan Natl Yang Ming Univ Dept Biomed Imaging & Radiol Sci Taipei Taiwan Biodivers Res Ctr Acad Sinica Taipei Taiwan

ISBN: (纸本)9781479900206

Previously we have developed a feature selection mechanism (Fuzzy Systems - Feature Attenuating Gates, FS-FAG), which cannot deal with very high dimensional data in an efficient manner. To address this issue, in this study we introduce a divide-and-conquer strategy for selection of marker genes for high dimensional cancer microarray data. This is a hierarchical system, which can be used with very high dimensional data. To demonstrate the effectiveness of the proposed scheme, we use two sets of microarray data under different experimental conditions. We examine the variations in the selected number of genes, the number of the final sets of marker genes, and the discriminating power of cancer subtypes of the selected marker genes. Experimental results demonstrate that proposed method indeed selects marker genes with good discriminating power for different cancer subtypes.

关键词： microarray data marker genes divide-and-conquer

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