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How many differentially expressed genes: A perspective from the comparison of genotypic and phenotypic distances

GENOMICS 2018年第1期110卷 67-73页

作者： Zhao, Bi Erwin, Aqeela Xue, Bin Univ S Florida Coll Arts & Sci Sch Nat Sci & Math Dept Cell Biol Microbiol & Mol Biol 4202 East Fowler AveISA2015 Tampa FL 33620 USA

Identifying differentially expressed genes is critical in microarray data analysis. Many methods have been developed by combining p-value, fold-change, and various statistical models to determine these genes. When using these methods, it is necessary to set up various pre-determined cutoff values. However, many of these cutoff values are somewhat arbitrary and may not have clear connections to biology. In this study, a genetic distance method based on gene expression level was developed to analyze eight sets of microarray data extracted from the GEO database. Since the genes used in distance calculation have been ranked by fold-change, the genetic distance becomes more stable when adding more genes in the calculation, indicating there is an optimal set of genes which are sufficient to characterize the stable difference between samples. This set of genes is differentially expressed genes representing both the genotypic and phenotypic differences between samples.

关键词： microarray data analysis Differentially expressed genes Genetic distance Genotypically and phenotypically significant DEGs gpsDEGs

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An improved Binary Particle Swarm Optimization (iBPSO) for Gene Selection and Cancer Classification using DNA microarrays

An improved Binary Particle Swarm Optimization (iBPSO) for G...

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Conference on Information and Communication Technology (CICT)

作者： Jain, Indu Jaint, Vinod Kumar Jain, Renu Jiwaji Univ Sch Math & Allied Sci SOMAAS Gwalior 474006 MP India PDPM IIITDM Comp Sci & Engn Discipline Jabalpur 482005 MP India

ISBN: (纸本)9781538682159

DNA microarrays enable the detection of genetic changes attributable to cancer by simultaneously analyzing the expression of thousands of genes. However, the identification of most relevant genes from thousands of gene expressions available in each biological sample, for cancer classification pose a great challenge. Although researchers have applied BPSO based wrapper approaches to get most relevant genes prior to cancer classification, these approaches didn't achieve good classification accuracy due to the premature convergence caused by local stagnation problem. This paper proposes an improved Binary Particle Swarm Optimization (iBPSO) to tackle these issues. The proposed iBPSO based wrapper is examined using Naive-Bayes (NB), k-Nearest Neighbor (kNN), and Support Vector Machines (SVM) classifiers with stratified 5-fold cross-validation. The proposed iBPSO exhibited its efficacy in terms of classification accuracy and the number of selected genes in comparison to standard BPSO on six benchmark cancer microarray datasets. Our proposed iBPSO also effectively escapes from local minima stagnation.

关键词： microarray data analysis cancer classification improved binary particle swarm optimization (iBPSO) gene selection

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RelSim: An integrated method to identify disease genes using gene expression profiles and PPIN based similarity measure

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INFORMATION SCIENCES 2017年 384卷 110-125页

作者： Maji, Pradipta Shah, Ekta Paul, Sushmita Indian Stat Inst Machine Intelligence Unit Biomed Imaging & Bioinformat Lab Kolkata India Indian Inst Technol Dept Biol Jodhpur Rajasthan India

One of the important problems in functional genomics is how to select the disease genes. In this regard, the paper presents a new gene selection algorithm, termed as RelSim, to identify disease genes. It integrates judiciously the information of gene expression profiles and protein-protein interaction networks. A new similarity measure is introduced to compute the functional similarity between two genes. It is based on the information of protein-protein interaction networks. The new similarity measure offers an efficient way to calculate the functional similarity between two genes. The proposed algorithm selects a set of genes as disease genes, considering both microarray and protein-protein interaction data, by maximizing the relevance and functional similarity of the selected genes. While gene expression profiles are used to identify differentially expressed genes, the protein protein interaction networks help to compute the functional similarity among genes. The performance of the proposed algorithm, along with a comparison with other related methods, is demonstrated on several colon cancer data sets. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Disease gene identification microarray data analysis Feature selection Protein-protein interaction networks

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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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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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Gene selection for microarray cancer classification using a new evolutionary method employing artificial intelligence concepts

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GENOMICS 2017年第2期109卷 91-107页

作者： Dashtban, M. Balafar, Mohammadali Univ Tabriz Fac Elect & Comp Engn Dept Comp Engn Tabriz Iran

Gene selection is a demanding task for microarray data analysis. The diverse complexity of different cancers makes this issue still challenging. In this study, a novel evolutionary method based on genetic algorithms and artificial intelligence is proposed to identify predictive genes for cancer classification. A filter method was first applied to reduce the dimensionality of feature space followed by employing an integer-coded genetic algorithm with dynamic-length genotype, intelligent parameter settings, and modified operators. The algorithmic behaviors including convergence trends, mutation and crossover rate changes, and running time were studied, conceptually discussed, and shown to be coherent with literature findings. Two well-known filter methods, Laplacian and Fisher score, were examined considering similarities, the quality of selected genes, and their influences on the evolutionary approach. Several statistical tests concerning choice of classifier, choice of clataset, and choice of filter method were performed, and they revealed some significant differences between the performance of different classifiers and filter methods over datasets. The proposed method was benchmarked upon five popular high -dimensional cancer datasets;for each, top explored genes were reported. Comparing the experimental results with several state-of-the-art methods revealed that the proposed method outperforms previous methods in DLBCL dataset. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Gene selection Cancer classification microarray data analysis Intelligent Dynamic Algorithm Random-restart hill climbing Reinforcement learning Penalizing strategy Cut and splice crossover Self-refinement strategy Feature selection

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analysis of Imputation Algorithms for microarray Gene Expression data 1

Analysis of Imputation Algorithms for Microarray Gene Expres...

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International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT)

作者： Shashirekha, H. L. Wani, Agaz Hussain Mangalore Univ Dept Comp Sci Mangalore 574199 India

ISBN: (纸本)9781467392235

microarray technology makes it possible to measure expression level of thousands of genes simultaneously in an efficient and inexpensive manner. However, due to various complexities in processing microarrays, expression information of various genes may be missing due to unreliable measurements. The occurrence of missing values in gene expression data can adversely affect downstream analyses such as clustering, dimensionality reduction etc. Different algorithms have been developed to estimate the missing values in different data sets and none of these algorithm works well with all the data sets. In this work, we explore the possible application of Mutual Nearest Neighbor (MNN) algorithm to impute the missing values, which shows comparable results with other well know imputation algorithms. We also have explored five different methods for missing value imputation namely Row Average Imputation, Mean Imputation, Median Imputation, k-Nearest Neighbor Imputation and combination of kNN based feature selection (kNNFS) and kNN -based imputation. The experiments are carried out on very high dimensional gene expression data such as Notterman Carcinoma and Notterman Adenocarcinoma data and the results are illustrated.

关键词： Imputation microarray data analysis Gene Expression data Missing Value Estimation

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Restricted Boltzmann Machines for Unsupervised Feature Selection with Partial Least Square Feature Extractor for microarray datasets 9

Restricted Boltzmann Machines for Unsupervised Feature Selec...

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9th International Conference on Advanced Computer Science and Information Systems (ICACSIS)

作者： Sutawika, Lintang Adyuta Wasito, Ito Univ Indonesia Fac Comp Sci Depok Indonesia

ISBN: (纸本)9781538631720

Feature selection is a key component in microarray data analysis. This is due to the fact that microarray datasets consists of features that are far exceed the number of instances. High dimensional data are also known to contain significant amount of noise and irrelevant variables that do not contribute to classification tasks and may even hinder classification performance. In this paper, a feature selection method which consists of two stages is proposed. At the first step, feature selection is done through a stacked Restricted Boltzmann Machines by means of comparing the error between reconstructed data and the original data. The next stage will use Partial Least Square to extract synthesis features from the previously selected features that will be then used for classification. The performance of the proposed method is done through the classification of ten microarray datasets that are widely used. The proposed model is able to out perform state-of-the-art in 2 datasets, namely 82.11% for GLIOMA and 72.39% for Breast datasets.

关键词： restricted boltzmann machine deep learning microarray data analysis gene expression

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Measuring the Inter-sample Heterogeneity by Dynamic PCA Biplot

Measuring the Inter-sample Heterogeneity by Dynamic PCA Bipl...

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Biological Ontologies and Knowledge Bases Workshop at IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM)

作者： Zhao, Bi Xue, Bin Univ S Florida Coll Arts & Sci Dept Cell Biol Microbiol & Mol Biol Tampa FL 33620 USA

ISBN: (纸本)9781509030507

In microarray/RNAseq experiments, different samples used in the same experiment may have significant levels of heterogeneity. Here, heterogeneity refers to the unique temporospatial composition of various cells in different samples. Consequently, the experimental results and subsequent data analysis may be seriously biased. To minimize the influence of heterogeneous samples in data analysis, the first step is to identify heterogeneous samples by measuring the inter- sample heterogeneity. Principal component analysis (PCA) biplot is often used for this purpose. However, the PCAbiplot is normally obtained from one set of differentially expressed genes, indicating the biplot is static and may not represent the heterogeneity dynamically. Therefore, a novel dynamic PCA biplot was developed in this project, and the advantage of this new method was exemplified in two GEO datasets.

关键词： microarray data analysis sample heterogeneity genotypic distance gpsDEG

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analysis of Gene Expression Discretization Techniques in microarray Biclustering 5th

Analysis of Gene Expression Discretization Techniques in Mic...

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5th International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO)

作者： Dussaut, J. S. Gallo, C. A. Carballido, J. A. Ponzoni, I. Univ Nacl Sur Inst Ciencias & Ingn Computac CONICET Bahia Blanca Buenos Aires Argentina

ISBN: (纸本)9783319561547;9783319561530

Gene expression biclustering analysis is a commonly used technique to see the interaction between genes under certain experiments or conditions. More specifically in the study of diseases, these methods are used to compare control and affected data in order to identify the involved or relevant genes. In some cases, discretization is needed for these algorithms to work correctly. In this context, the choice of the discretization method is extremely important and has a major impact on the outcome. In this work we analyze several discretization methods for Alzheimer Disease (AD) gene expression data and compare the results of a state-of-art biclustering algorithm after each discretization. The comparison reveals that biclusters obtained from discretized expression values achieve a major coverage and overall enrichment than biclusters generated from real-valued expression data. In a particular experiment, a clustering-based discretization method overcomes all competing techniques for the dataset under study, in statistical terms.

关键词： Discretization data preprocessing microarray data analysis Machine learning

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