检索结果-内蒙古大学图书馆

A CYCLIC HYBRID METHOD TO SELECT A SMALLER SUBSET OF INFORMATIVE GENES FOR CANCER CLASSIFICATION

INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2009年第8期5卷 2189-2202页

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 Fac Comp Sci & Informat Syst Dept Software Engn Skudai 81310 Johore Malaysia

microarray data are expected to be useful for cancer classification. The main problem that needs to be addressed is the selection of a smaller subset of genes from the thousands of genes in the data that contributes to a cancer disease. This selection process is difficult due to many irrelevant genes, noisy data, and the availability of the small number of samples compared to the huge number of genes (higher-dimensional data). Hence, this paper aims to select a smaller subset of informative genes that is the most relevant for the cancer classification. To achieve the aim, a cyclic hybrid method has been proposed. Five real microarray data sets are used to test the effectiveness of the method. Experimental results show that the performance of the proposed method is superior to other experimental methods and related previous works in terms of classification accuracy and the number of selected genes. In addition, a scatter gene graph and a list of informative genes in the best gene subsets are also presented for biological usage.

关键词： Cancer classification Gene selection Genetic algorithm Cyclic hybrid method microarray data

来源：评论

学校读者我要写书评

暂无评论

Late acceptance hill climbing aided chaotic harmony search for feature selection: An empirical analysis on medical data

引用

EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期221卷

作者： Naskar, Anurup Pramanik, Rishav Hossain, S. K. Sabbir Mirjalili, Seyedali Sarkar, Ram Jadavpur Univ Dept Comp Sci & Engn Kolkata 700032 India Torrens Univ Ctr Artificial Intelligence Res & Optimizat Brisbane Qld 4006 Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea Obuda Univ Univ Res & Innovat Ctr Budapest Hungary

In today's era of data-driven digital society, there is a huge demand for optimized solutions that essentially reduce the cost of operation, thereby aiming to increase productivity. Processing a huge amount of data, like the microarray based gene expression data, using machine learning and data mining algorithms has certain limitations in terms of memory and time requirements. This would be more concerning, when a dataset comes with redundant and non-important information. For example, many report-based medical datasets have several non-informative attributes which mislead the classification algorithms. To this end, researchers have been developing several feature selection algorithms that try to discard the redundant information from the raw datasets before feeding them to machine learning algorithms. Metaheuristic based optimization algorithms provide an excellent option to solve feature selection problems. In this paper, we propose a music-inspired harmony search (HS) algorithm based wrapper feature selection method. At the beginning, we use a chaotic mapping to initialize the population of the HS algorithm in order to better coverage of the search space. Further to complement the inferior exploitation of the HS algorithm, we integrate it with the Late Acceptance Hill Climbing (LAHC) method. Thus the combination of these two algorithms provides a good balance between the exploration and exploitation of the HS algorithm. We evaluate the proposed feature selection method on 15 UCI datasets and the obtained results are found to be better than many state-of-the-art methods both in terms of the classification accuracy and the number of features selected. To evaluate the effectiveness of our algorithm, we utilize a combination of precision, recall, F1 score, fitness value, and execution time as performance indicators. These metrics enable us to obtain a comprehensive assessment of the algorithm's abilities and limitations. We also apply our method on 3 microarray based

关键词： Feature selection microarray data Harmony search Late Acceptance Hill Climbing Metaheuristics COVID-19 data Optimization Algorithm

来源：评论

学校读者我要写书评

暂无评论

EDGE³: A web-based solution for management and analysis of Agilent two color microarray experiments

引用

BMC BIOINFORMATICS 2009年第1期10卷 280-280页

作者： Vollrath, Aaron L. Smith, Adam A. Craven, Mark Bradfield, Christopher A. Univ Wisconsin McArdle Lab Canc Res Sch Med & Publ Hlth Madison WI 53706 USA Univ Wisconsin Dept Biostat & Med Informat Madison WI 53706 USA Univ Wisconsin Dept Comp Sci Madison WI 53706 USA

Background: The ability to generate transcriptional data on the scale of entire genomes has been a boon both in the improvement of biological understanding and in the amount of data generated. The latter, the amount of data generated, has implications when it comes to effective storage, analysis and sharing of these data. A number of software tools have been developed to store, analyze, and share microarray data. However, a majority of these tools do not offer all of these features nor do they specifically target the commonly used two color Agilent DNA microarray platform. Thus, the motivating factor for the development of EDGE(3) was to incorporate the storage, analysis and sharing of microarray data in a manner that would provide a means for research groups to collaborate on Agilent-based microarray experiments without a large investment in software-related expenditures or extensive training of end-users. Results: EDGE(3) has been developed with two major functions in mind. The first function is to provide a workflow process for the generation of microarray data by a research laboratory or a microarray facility. The second is to store, analyze, and share microarray data in a manner that doesn't require complicated software. To satisfy the first function, EDGE(3) has been developed as a means to establish a well defined experimental workflow and information system for microarray generation. To satisfy the second function, the software application utilized as the user interface of EDGE(3) is a web browser. Within the web browser, a user is able to access the entire functionality, including, but not limited to, the ability to perform a number of bioinformatics based analyses, collaborate between research groups through a user-based security model, and access to the raw data files and quality control files generated by the software used to extract the signals from an array image. Conclusion: Here, we present EDGE(3), an open-source, web-based application that allows f

关键词： microarray data Query Object Agilent Feature Extraction Software data Analysis Section Array Object

来源：评论

学校读者我要写书评

暂无评论

PathEx: a novel multi factors based datasets selector web tool

引用

BMC BIOINFORMATICS 2010年第1期11卷 1-9页

作者： Bareke, Eric Pierre, Michael Gaigneaux, Anthoula De Meulder, Bertrand Depiereux, Sophie Habra, Naji Depiereux, Eric Univ Namur FUNDP Mol Biol Res Unit URBM Namur Belgium Univ Namur FUNDP Res Ctr Informat Syst Engn PReCISE Namur Belgium

Background: microarray experiments have become very popular in life science research. However, if such experiments are only considered independently, the possibilities for analysis and interpretation of many life science phenomena are reduced. The accumulation of publicly available data provides biomedical researchers with a valuable opportunity to either discover new phenomena or improve the interpretation and validation of other phenomena that partially understood or well known. This can only be achieved by intelligently exploiting this rich mine of information. Description: Considering that technologies like microarrays remain prohibitively expensive for researchers with limited means to order their own experimental chips, it would be beneficial to re-use previously published microarray data. For certain researchers interested in finding gene groups (requiring many replicates), there is a great need for tools to help them to select appropriate datasets for analysis. These tools may be effective, if and only if, they are able to re-use previously deposited experiments or to create new experiments not initially envisioned by the depositors. However, the generation of new experiments requires that all published microarray data be completely annotated, which is not currently the case. Thus, we propose the PathEx approach. Conclusion: This paper presents PathEx, a human-focused web solution built around a two-component system: one database component, enriched with relevant biological information (expression array, omics data, literature) from different sources, and another component comprising sophisticated web interfaces that allow users to perform complex dataset building queries on the contents integrated into the PathEx database.

关键词： microarray data Sample File Content Logic Publish microarray data Chip Definition Files

来源：评论

学校读者我要写书评

暂无评论

Bayesian Model Selection for High-dimensional High-throughput data

Bayesian Model Selection for High-dimensional High-throughpu...

引用

作者： Joshi, Adarsh Texas A&M University

Bayesian methods are often criticized on the grounds of subjectivity. Furthermore, misspecified priors can have a deleterious effect on Bayesian inference. Noting that model selection is effectively a test of many hypotheses, Dr. Valen E. Johnson sought to eliminate the need of prior specification by computing Bayes’ factors from frequentist test statistics. In his pioneering work that was published in the year 2005, Dr. Johnson proposed using so-called local priors for computing Bayes? factors from test statistics. Dr. Johnson and Dr. Jianhua Hu used Bayes’ factors for model selection in a linear model setting. In an independent work, Dr. Johnson and another colleage, David Rossell, investigated two families of non-local priors for testing the regression parameter in a linear model setting. These non-local priors enable greater separation between the theories of null and alternative hypotheses. In this dissertation, I extend model selection based on Bayes’ factors and use nonlocal priors to define Bayes’ factors based on test statistics. With these priors, I have been able to reduce the problem of prior specification to setting to just one scaling parameter. That scaling parameter can be easily set, for example, on the basis of frequentist operating characteristics of the corresponding Bayes’ factors. Furthermore, the loss of information by basing a Bayes’ factors on a test statistic is minimal. Along with Dr. Johnson and Dr. Hu, I used the Bayes’ factors based on the likelihood ratio statistic to develop a method for clustering gene expression data. This method has performed well in both simulated examples and real datasets. An outline of that work is also included in this dissertation. Further, I extend the clustering model to a subclass of the decomposable graphical model class, which is more appropriate for genotype data sets, such as single-nucleotide polymorphism (SNP) data. Efficient FORTRAN programming has enabled me to apply the methodology to hundreds of

关键词： Bayes factors Bayes factors based on test statistics Bayesian Graphs MCMC Objective BayesianAnalysis Bayesian Model Selection microarray data Single-nucleotide polymorphism (SNP) data Thesis

来源：评论

学校读者我要写书评

暂无评论

Dimension Reduction and Classifier-Based Feature Selection for Oversampled Gene Expression data and Cancer Classification

引用

PROCESSES 2023年第7期11卷 1940页

作者： Petinrin, Olutomilayo Olayemi Saeed, Faisal Salim, Naomie Toseef, Muhammad Liu, Zhe Muyide, Ibukun Omotayo City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Birmingham City Univ Sch Comp & Digital Technol Dept Comp & Data Sci DAAI Res Grp Birmingham B4 7XG England Univ Teknol Malaysia Ibnu Sina Inst Sci & Ind Res UTM Big Data Ctr Johor Baharu 81310 Johor Malaysia Georgia Inst Technol Coll Comp Atlanta GA 30332 USA

Gene expression data are usually known for having a large number of features. Usually, some of these features are irrelevant and redundant. However, in some cases, all features, despite being numerous, show high importance and contribute to the data analysis. In a similar fashion, gene expression data sometimes have limited instances with a high rate of imbalance among the classes. This can limit the exposure of a classification model to instances of different categories, thereby influencing the performance of the model. In this study, we proposed a cancer detection approach that utilized data preprocessing techniques such as oversampling, feature selection, and classification models. The study used SVMSMOTE for the oversampling of the six examined datasets. Further, we examined different techniques for feature selection using dimension reduction methods and classifier-based feature ranking and selection. We trained six machine learning algorithms, using repeated 5-fold cross-validation on different microarray datasets. The performance of the algorithms differed based on the data and feature reduction technique used.

关键词： cancer classification gene expression machine learning microarray data sampling methods

来源：评论

学校读者我要写书评

暂无评论

Multivariate dependence and genetic networks inference

引用

IET SYSTEMS BIOLOGY 2010年第6期4卷 428-440页

作者： Margolin, A. A. Wang, K. Califano, A. Nemenman, I. Broad Inst Harvard & MIT Canc Program Cambridge MA 02142 USA Columbia Univ Med Ctr Joint Ctr Syst Biol New York NY 10032 USA Pfizer Oncol Res Unit San Diego CA 92121 USA Emory Univ Dept Phys Computat & Life Sci Strateg Initiat Atlanta GA 30322 USA Emory Univ Dept Biol Computat & Life Sci Strateg Initiat Atlanta GA 30322 USA Emory Univ Atlanta GA 30322 USA

A critical task in systems biology is the identification of genes that interact to control cellular processes by transcriptional activation of a set of target genes. Many methods have been developed that use statistical correlations in high-throughput data sets to infer such interactions. However, cellular pathways are highly cooperative, often requiring the joint effect of many molecules. Few methods have been proposed to explicitly identify such higher-order interactions, partially due to the fact that the notion of multivariate statistical dependence itself remains imprecisely defined. The authors define the concept of dependence among multiple variables using maximum entropy techniques and introduce computational tests for their identification. Synthetic network results reveal that this procedure uncovers dependencies even in undersampled regimes, when the joint probability distribution cannot be reliably estimated. Analysis of microarray data from human B cells reveals that third-order statistics, but not second-order ones, uncover relationships between genes that interact in a pathway to cooperatively regulate a common set of targets.

关键词： genetic networks inference Probability theory, stochastic processes, and statistics systems biology cellular pathways correlation methods Biomolecular structure, configuration, conformation, and active sites third-order statistics higher order statistics maximum entropy methods transcriptional activation molecular biophysics control cellular processes multivariate dependence Physics of subcellular structures microarray data human B cells joint probability distribution statistical correlations synthetic network cellular biophysics Biomolecular dynamics, molecular probes, molecular pattern recognition target genes maximum entropy techniques gene identification genetics

来源：评论

学校读者我要写书评

暂无评论

Dynamic modeling of cis-regulatory circuits and gene expression prediction via cross-gene identification

引用

BMC BIOINFORMATICS 2005年第1期6卷 258-258页

作者： Lin, LH Lee, HC Li, WH Chen, BS Natl Tsing Hua Univ Lab Syst Biol Hsinchu 300 Taiwan Natl Tsing Hua Univ Dept Life Sci Hsinchu 300 Taiwan Natl Tsing Hua Univ Inst Bioinformat & Struct Biol Hsinchu 300 Taiwan Univ Chicago Dept Ecol & Evolut Chicago IL 60637 USA Acad Sinica Genom Res Ctr Taipei 115 Taiwan

Background: Gene expression programs depend on recognition of cis elements in promoter region of target genes by transcription factors (TFs), but how TFs regulate gene expression via recognition of cis elements is still not clear. To study this issue, we define the cis-regulatory circuit of a gene as a system that consists of its cis elements and the interactions among their recognizing TFs and develop a dynamic model to study the functional architecture and dynamics of the circuit. This is in contrast to traditional approaches where a cis-regulatory circuit is constructed by a mutagenesis or motif-deletion scheme. We estimate the regulatory functions of cis-regulatory circuits using microarray data. Results: A novel cross-gene identification scheme is proposed to infer how multiple TFs coordinate to regulate gene transcription in the yeast cell cycle and to uncover hidden regulatory functions of a cis-regulatory circuit. Some advantages of this approach over most current methods are that it is based on data obtained from intact cis-regulatory circuits and that a dynamic model can quantitatively characterize the regulatory function of each TF and the interactions among the TFs. Our method may also be applicable to other genes if their expression profiles have been examined for a sufficiently long time. Conclusion: In this study, we have developed a dynamic model to reconstruct cis-regulatory circuits and a cross-gene identification scheme to estimate the regulatory functions of the TFs that control the regulation of the genes under study. We have applied this method to cell cycle genes because the available expression profiles for these genes are long enough. Our method not only can quantify the regulatory strengths and synergy of the TFs but also can predict the expression profile of any gene having a subset of the cis elements studied.

关键词： microarray data Regulatory Function Interaction Function Cell Cycle Gene Yeast Cell Cycle

来源：评论

学校读者我要写书评

暂无评论

Optimizing microarray cancer gene selection using swarm intelligence: Recent developments and an exploratory study

引用

EGYPTIAN INFORMATICS JOURNAL 2023年第4期24卷

作者： Isuwa, Jeremiah Abdullahi, Mohammed Ali, Yusuf Sahabi Hassan, Ibrahim Hayatu Buba, Jesse Romeo Aliyu, Ibrahim Kim, Jinsul Oyelade, Olaide Nathaniel Fed Univ Kashere Dept Comp Sci Gombe Nigeria Ahmadu Bello Univ Dept Comp Sci Zaria Nigeria Ahmadu Bello Univ Inst Agr Res Zaria Nigeria Univ Maiduguri Dept Med Lab Sci Maiduguri Nigeria Chonnam Natl Univ Dept ICT Convergence Syst Engn Gwangju 61186 South Korea

microarray data represents a valuable tool for the identification of biomarkers associated with diseases and other biological conditions. Genes, in particular, are a type of biomarker that holds great importance for the identi-fication and understanding of various types of tumors, including brain, lung, and breast cancers. However, a significant portion of these cancer genes are not directly associated with the target disease, which can lead to challenges during analysis, such as increased computational complexity, poor generalization, and decreased classification accuracy, among others. To address this issue, a range of techniques and algorithms have been developed to optimize the selection of the most relevant subset of cancer genes. One highly effective approach to handle this challenge is the use of Swarm Intelligent (SI) algorithms, which are known for their efficiency and effectiveness as global search agents. In this paper, we present two distinct but related sections. First, we conduct a survey of current literature from 2019 to the present, on the use of SI algorithms for optimizing the selection of an optimal subset of cancer genes. Secondly, based on the analysis and findings from the first part, a presentation of an experimental study that evaluates the efficacy of four classical SI algorithms -Particle Swarm Optimization (PSO), Salp Swarm Optimization (SSA), Firefly Algorithm (FA), and Cuckoo Search (CS) - for optimizing the selection of relevant genes in three different cancer datasets. For the experimental study, we used the Chi-square, Mutual Information, and ANOVA filter methods to individually select 100, 200, and 500 relevant genes from the identified cancer datasets. We then passed these genes as input to each of the SI algorithms. The results of the study indicate that diverse filter-wrapper combinations can effectively address the challenge of selecting cancer genes across various datasets.

关键词： Swarm intelligence Filter methods microarray data Feature selection Machine learning

来源：评论

学校读者我要写书评

暂无评论

EzArray: A web-based highly automated Affymetrix expression array data management and analysis system

引用

BMC BIOINFORMATICS 2008年第1期9卷 46-46页

作者： Zhu, Yuerong Zhu, Yuelin Xu, Wei BioInfoRx Inc Middleton WI 53562 USA Georgetown Univ Dept Oncol Washington DC 20057 USA Univ Wisconsin Dept Oncol Madison WI 53706 USA

Background: Though microarray experiments are very popular in life science research, managing and analyzing microarray data are still challenging tasks for many biologists. Most microarray programs require users to have sophisticated knowledge of mathematics, statistics and computer skills for usage. With accumulating microarray data deposited in public databases, easy-to-use programs to re-analyze previously published microarray data are in high demand. Results: EzArray is a web-based Affymetrix expression array data management and analysis system for researchers who need to organize microarray data efficiently and get data analyzed instantly. EzArray organizes microarray data into projects that can be analyzed online with predefined or custom procedures. EzArray performs data preprocessing and detection of differentially expressed genes with statistical methods. All analysis procedures are optimized and highly automated so that even novice users with limited pre-knowledge of microarray data analysis can complete initial analysis quickly. Since all input files, analysis parameters, and executed scripts can be downloaded, EzArray provides maximum reproducibility for each analysis. In addition, EzArray integrates with Gene Expression Omnibus (GEO) and allows instantaneous re-analysis of published array data. Conclusion: EzArray is a novel Affymetrix expression array data analysis and sharing system. EzArray provides easy-to-use tools for re-analyzing published microarray data and will help both novice and experienced users perform initial analysis of their microarray data from the location of data storage. We believe EzArray will be a useful system for facilities with microarray services and laboratories with multiple members involved in microarray data analysis. EzArray is freely available from http://***/.

关键词： microarray data Gene Expression Omnibus microarray data Analysis Bioconductor Package Novice User

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：