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microarray gene expression profiling provides insights into functions of TIPE2 in HBV-related apoptosis

MOLECULAR IMMUNOLOGY 2021年 131卷 137-143页

作者： Cui, Jian Zhang, Na Liu, Ying Zhang, Lei Gao, Chengjiang Liu, Suxia Shandong Univ Sch Basic Med Sci Dept Immunol 44 Wenhua West Rd Jinan 250012 Peoples R China Shandong Univ Sch Basic Med Sci Key Lab Infect & Immun Shandong Prov Jinan Peoples R China

Tumor necrosis factor-a-induced protein-8 like-2 (TNFAIP8L2, TIPE2), a member of TNFAIP8 family, functions as a regulator in inflammation. Our previous studies showed that TIPE2 can negatively regulate HBV-specific CD8+ T lymphocyte functions. But the effect of TIPE2 on the apoptosis of HBV-infected hepatocytes which is very important for eliminating viruses remains unclear. Using gene expression microarray analysis, we find that TIPE2 deficiency can regulate the expression of apoptotic genes in liver tissues from HBV hydrodynamic injection (HI) mouse model. TIPE2 protein was detected in TUNEL staining positive hepatocytes in HBV-infected C57 mice. Interestingly, the TIPE2 expressed hepatocytes were just the HBV infected cells. Furthermore, TIPE2 upregulates the mRNA levels of FasL, Bim and TNFRsF1b which promote cells death, when TIPE2 was transfected into HepG2 cells in vitro. As a result, TIPE2 overexpression cells showed a higher number of apoptotic cells and increased level of cleavage caspase3 compared to controls. Those results indicate that TIPE2 participates in HBV infection by regulating apoptosis of virus-infected hepatocytes.

关键词： TNFAIP8L2 (TIPE2) microarray gene expression Hepatitis B Apoptosis Inflammation

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Consensus analysis of multiple classifiers using non-repetitive variables: Diagnostic application to microarray gene expression data

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COMPUTATIONAL BIOLOGY AND CHEMISTRY 2007年第1期31卷 48-56页

作者： Su, Zhenqiang Hong, Huixiao Perkins, Roger Shao, Xueguang Cai, Wensheng Tong, Weida Univ Sci & Technol China Dept Chem Hefei 230026 Anhui Peoples R China Natl Ctr Toxicol Res Div Bioinformat Z Tech FDA Jefferson AR 72079 USA Nankai Univ Dept Chem Tianjin 300071 Peoples R China US FDA Natl Ctr Toxicol Res Ctr Toxicoinformat Jefferson AR 72079 USA

Class prediction based on DNA microarray data has been emerged as one of the most important application of bioinformatics for diagnostics/prognostics. Robust classifiers are needed that use most biologically relevant genes embedded in the data. A consensus approach that combines multiple classifiers has attributes that mitigate this difficulty compared to a single classifier. A new classification method named as consensus analysis of multiple classifiers using non-repetitive variables (CAMCUN) was proposed for the analysis of hyper-dimensional gene expression data. The CAMCUN method combined multiple classifiers, each of which was built from distinct, non-repeated genes that were selected for effectiveness in class differentiation. Thus, the CAMCUN utilized most biologically relevant genes in the final classifier. The CAMCUN algorithm was demonstrated to give consistently more accurate predictions for two well-known datasets for prostate cancer and leukemia. Importantly, the CAMCUN algorithm employed an integrated 10-fold cross-validation and randomization test to assess the degree of confidence of the predictions for unknown samples. (c) 2007 Elsevier Ltd. All rights reserved.

关键词： consensus approach microarray gene expression diagnostics prognostics

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Leukemia and small round blue-cell tumor cancer detection using microarray gene expression data set: Combining data dimension reduction and variable selection technique

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2014年 139卷 6-14页

作者： Karimi, Sadegh Farrokhnia, Maryam Persian Gulf Univ Coll Sci Dept Chem Bushehr Iran Bushehr Univ Med Sci Persian Gulf Marine Biotechnol Res Ctr Bushehr Iran

Using gene expression data in cancer classification plays an important role for solving the fundamental problems relating to cancer diagnosis. Because of high throughput of gene expression data for healthy and patient samples, a variable selection method can be applied to reduce complexity of the model and improve the classification performance. Since variable selection procedures pose a risk of over-fitting, when a large number of variables with respect to sample are used, we have proposed a method for coupling data dimension reduction and variable selection in the present study. This approach uses the concept of variable clustering for the original data set. Significant components of local principal component analysis models have just been retained from all clusters. Then, the variable selection algorithm is performed on these locally derived principal component variables. The proposed algorithm has been evaluated on two gene expression data sets;namely, acute Leukemia and small round blue-cell tumor (SRBCT). Our results confirmed that the classification models achieved on the reduced data were better than those obtained on the entire microarray gene expression profile. (C) 2014 Elsevier B.V. All rights reserved.

关键词： microarray gene expression Linear discriminant analysis Clustering of variable Kohonen self-organizing map genetic algorithm

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A comparative study of machine learning and deep learning algorithms to classify cancer types based on microarray gene expression data

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PEERJ COMPUTER SCIENCE 2020年第4期6卷 e270页

作者： Tabares-Soto, Reinel Orozco-Arias, Simon Romero-Cano, Victor Segovia Bucheli, Vanesa Luis Rodriguez-Sotelo, Jose Felipe Jimenez-Varon, Cristian Univ Autonoma Manizales Dept Elect & Automat Manizales Caldas Colombia Univ Autonoma Manizales Dept Comp Sci Manizales Caldas Colombia Univ Caldas Dept Syst & Informat Manizales Caldas Colombia Univ Autonoma Occidente Dept Automat & Elect Cali Valle Del Cauca Colombia Dokuz Eylul Univ Izmir Int Biomed & Genome Inst Izmir Turkey Univ Autonoma Manizales Dept Phys & Math Manizales Caldas Colombia

Cancer classification is a topic of major interest in medicine since it allows accurate and efficient diagnosis and facilitates a successful outcome in medical treatments. Previous studies have classified human tumors using a large-scale RNA profiling and supervised Machine Learning (ML) algorithms to construct a molecular-based classification of carcinoma cells from breast, bladder, adenocarcinoma, colorectal, gastro esophagus, kidney, liver, lung, ovarian, pancreas, and prostate tumors. These datasets are collectively known as the 11_tumor database, although this database has been used in several works in the ML field, no comparative studies of different algorithms can be found in the literature. On the other hand, advances in both hardware and software technologies have fostered considerable improvements in the precision of solutions that use ML, such as Deep Learning (DL). In this study, we compare the most widely used algorithms in classical ML and DL to classify the tumors described in the 11_tumor database. We obtained tumor identification accuracies between 90.6% (Logistic Regression) and 94.43% (Convolutional Neural Networks) using k-fold cross-validation. Also, we show how a tuning process may or may not significantly improve algorithms' accuracies. Our results demonstrate an efficient and accurate classification method based on gene expression (microarray data) and ML/DL algorithms, which facilitates tumor type prediction in a multi-cancer-type scenario.

关键词： Machine Learning Deep Learning Cancer classification microarray gene expression 11_tumor database Bioinformatics

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Neuro-logistic Models Based on Evolutionary generalized Radial Basis Function for the microarray gene expression Classification Problem

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NEURAL PROCESSING LETTERS 2011年第2期34卷 117-131页

作者： Castano, A. Fernandez-Navarro, F. Hervas-Martinez, C. Gutierrez, P. A. Univ Cordoba Dept Comp Sci & Numer Anal Cordoba Spain Univ Pinar del Rio Dept Informat Pinar Del Rio Cuba

gene expression detection is a key bioinformatic problem which has been tackled as a classification problem of microarray gene expression, obtained by the light reflection analysis of genomic material. A typical microarray dataset may contain thousands of genes but only a small number of patterns (often less than two hundred). When the dataset presents these kinds of characteristics, state-of-the-art classification models show a high lack of performance. A two-stage algorithm has been proposed to successfully address the problem of microarray classification. In the first stage, two filter algorithms identify salient expression genes from thousands of genes. In the second stage, the proposed methodology is performed using selected gene subsets as new input variables. The methodology proposed is composed of a combination of Logistic Regression (LR) and Evolutionary generalized Radial Basis Function (EGRBF) neural networks which have shown to be highly accurate in previous research in the modeling of high-dimensional patterns. Finally, the results obtained are contrasted with nonparametric statistical tests and confirm good synergy between EGRBF and LR models.

关键词： Evolutionary algorithm generalized radial basis function Logistic regression Neural networks Classification microarray gene expression

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Asymmetric type II compound Laplace distribution and its application to microarray gene expression

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2012年第6期56卷 1396-1404页

作者： Punathumparambath, Bindu Kulathinal, Sangita George, Sebastian St Thomas College Dept Stat Pala Kerala India Ind Soc Educ & Dev Nasik India

In the present paper, the asymmetric type II compound Laplace distribution is introduced and various properties are studied. The maximum likelihood estimation procedure is employed to estimate the parameters of the proposed distribution and an algorithm in R package is developed to carry out the estimation. Simulation studies for various choices of parameter values are performed to validate the algorithm. Finally, we fit the asymmetric type II compound Laplace, asymmetric Laplace, and log-normal distributions to five microarray gene expression datasets and compare them. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Asymmetric Laplace distribution Asymmetric type II compound Laplace distribution microarray gene expression Symmetric type II compound Laplace distribution

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An expert system to classify microarray gene expression data using gene selection by decision tree

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EXPERT SYSTEMS WITH APPLICATIONS 2009年第5期36卷 9072-9081页

作者： Horng, Jorng-Tzong Wu, Li-Cheng Liu, Baw-Juine Kuo, Jun-Li Kuo, Wen-Horng Zhang, Jin-Jian Natl Cent Univ Dept Comp Sci & Informat Engn Chungli 32054 Taiwan Natl Cent Univ Inst Syst Biol & Bioinformat Chungli 32054 Taiwan Yuan Ze Univ Dept Comp Sci & Informat Engn Jhongli Taiwan Natl Cent Univ Coll Med Chungli 32054 Taiwan

gene selection call help the analysis of microarray gene expression data. However, it is very difficult to obtain a satisfactory classification result by machine learning techniques because of both the curse-of-dimensionality problem and the over-fitting problem. That is, the dimensions of the features are too large but the samples are too few. In this study, we designed an approach that attempts to avoid these two problems and then used it to select a small set of significant biomarker genes for diagnosis. Finally, we attempted to use these markers for the classification of cancer. This approach was tested the approach on a number of microarray datasets in order to demonstrate that it performs well and is both useful and reliable. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Expert system Machine learning Bioinformatics microarray gene expression Decision tree

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Candidate genes affecting Drosophila life span identified by integrating microarray gene expression analysis and QTL mapping

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MECHANISMS OF AGEING AND DEVELOPMENT 2007年第3期128卷 237-249页

作者： Lai, Chao-Qiang Parnell, Laurence D. Lyman, Richard F. Ordovas, Jose A. Mackay, Trudy F. C. Tufts Univ USDA Human Nutr Res Ctr Aging JMNutr & Genom Lab Boston MA 02111 USA N Carolina State Univ Dept Genet Raleigh NC 27695 USA N Carolina State Univ WM Keck Ctr Behav Biol Raleigh NC 27695 USA

The current increase in life expectancy observed in industrialized societies underscores the need to achieve a better understanding of the aging process that could help the development of effective strategies to achieve healthy aging. This will require not only identifying genes involved in the aging process, but also understanding how their effects are modulated by environmental factors, such as dietary intake and life style. Although the human genome has been sequenced, it may be impractical to study humans or other long-lived organisms to gain a mechanistic understanding about the aging process. Thus, short-lived animal models are essential to identifying the mechanisms and genes that affect the rate and quality of aging as a first step towards identifying genetic variants in humans. In this study, we investigated gene expression changes between two strains of Drosophila (Oregon and 2b) for which quantitative trait loci (QTLs) affecting life span were identified previously. We collected males and females from both strains at young and old ages, and assessed whole genome variation in transcript abundance using Aflymetrix geneChips. We observed 8217 probe sets with detectable transcripts. A total of 2371 probe sets, representing 2220 genes, exhibited significant changes in transcript abundance with age;and 839 probe sets were differentially expressed between Oregon and 2b. We focused on the 359 probe sets (representing 354 genes) that exhibited significant changes in gene expression both with age and between strains. We used these genes to integrate the analysis of microarray gene expression data, bioinformatics, and the results of genetic mapping studies reported previously, to identify 49 candidate genes and four pathways that could potentially be responsible for regulating life span and involved in the process of aging in Drosophila and humans. Published by Elsevier Ireland Ltd.

关键词： life span microarray gene expression QTL Drosophila aging

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Design and implementation of microarray gene expression markup language (MAGE-ML)

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GENOME BIOLOGY 2002年第9期3卷 research0046.1-research0046.9页

作者： Spellman, Paul T. Miller, Michael Stewart, Jason Troup, Charles Sarkans, Ugis Chervitz, Steve Bernhart, Derek Sherlock, Gavin Ball, Catherine Lepage, Marc Swiatek, Marcin Marks, W. L. Goncalves, Jason Markel, Scott Iordan, Daniel Shojatalab, Mohammadreza Pizarro, Angel White, Joe Hubley, Robert Deutsch, Eric Senger, Martin Aronow, Bruce J. Robinson, Alan Bassett, Doug Stoeckert, Christian J., Jr. Brazma, Alvis Univ Calif Berkeley Dept Cell & Mol Biol Berkeley CA 94720 USA Rosetta Biosoftware Kirkland WA 98034 USA Open Informat Albuquerque NM 87108 USA Biosci Res Agilent Technol Palo Alto CA 94304 USA EMBL Hinxton Outstn European Bioinformat Inst Cambridge CB10 1SD England Affymetrix Inc Emeryville CA 94608 USA Stanford Univ Dept Genet Sch Med Stanford CA 94305 USA Mol Min Corp Kingston ON K7K 2Z8 Canada Imaging Res Inc St Catharines ON L2S 3A1 Canada Iob Informat LLC La Jolla CA 92037 USA LION Biosci Inc San Diego CA 92121 USA Univ Penn Ctr Bioinformat Philadelphia PA 19104 USA Inst Genom Res Rockville MD 20850 USA Inst Syst Biol Seattle WA 98103 USA Univ Cincinnati Coll Med CHRF Cincinnati OH 45229 USA

Background: Meaningful exchange of microarray data is currently difficult because it is rare that published data provide sufficient information depth or are even in the same format from one publication to another. Only when data can be easily exchanged will the entire biological community be able to derive the full benefit from such microarray studies. Results: To this end we have developed three key ingredients towards standardizing the storage and exchange of microarray data. First, we have created a minimal information for the annotation of a microarray experiment (MIAME)-compliant conceptualization of microarray experiments modeled using the unified modeling language (UML) named MAGE-OM (microarray gene expression object model). Second, we have translated MAGE-OM into an XML-based data format, MAGE-ML, to facilitate the exchange of data. Third, some of us are now using MAGE (or its progenitors) in data production settings. Finally, we have developed a freely available software tool kit (MAGE-STK) that eases the integration of MAGE-ML into end users' systems. Conclusions: MAGE will help microarray data producers and users to exchange information by providing a common platform for data exchange, and MAGE-STK will make the adoption of MAGE easier.

关键词： Unify Modeling Language microarray Experiment Application Programmer Interface microarray gene expression Database Schema

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Mapping multi-class cancers and clinical outcomes prediction for multiple classifications of microarray gene expression data

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KOREAN JOURNAL OF CHEMICAL ENGINEERING 2009年第4期26卷 969-979页

作者： Kim, Yong Su Hwang, Sun Jin Oh, Jong Min Whang, Gye Dae Yoo, Chang Kyoo Kyung Hee Univ Ctr Environm Studies Dept Environm Sci & Engn Coll Engn Suwon 446701 South Korea

DNA microarray analysis of gene expression is useful for discriminating between the various subtypes of cancer, which is necessary for the accurate diagnosis and treatment of patients. Particularly, assigning biological samples into subclasses or obtaining detailed phenotypes is an important practical application for microarray gene expression profiles. In the present study, a hierarchical framework of a nonlinear mapping classification was developed for elucidating data and classifying multiclass cancers based on microarray data sets. This classification maps the gene expression profiles of multi-class cancers to the visualized latent space and predicts the clinical output through high-dimensional computational biology. The proposed method was used to interpret and analyze four leukemia subtypes from microarray data. The results demonstrate that, using a high-dimensional nonlinear mapping to extract biological insights from microarray data, the proposed method can identify leukemia subtypes on the basis of molecular-level monitoring and improve the interpretability of leukemia clinical outputs. Furthermore, this nonlinear mapping of cancer subtypes is used to establish a relationship between expression-based subclasses of leukemia tumors and leukemia patient treatment outcomes. The proposed method may be used to guide efficient and effective approaches for the treatment of leukemia subclasses.

关键词： Bioinformatics Cancer Classification Clinical Outcome Hierarchical Framework generative Topographic Mapping microarray gene expression

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