检索结果-内蒙古大学图书馆

Recurrent neural network-based modeling of gene regulatory network using elephant swarm water search algorithm

JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY 2017年第4期15卷 1750016-1750016页

作者： Mandal, Sudip Saha, Goutam Pal, Rajat Kumar Global Inst Management & Technol Elect & Commun Engn Dept Krishnanagar 741102 W Bengal India North Eastern Hill Univ Informat Technol Dept Shillong 793022 Meghalaya India Univ Calcutta Dept Comp Sci & Engn Acharya Prafulla Chandra Siksha Prangan JD-2 Kolkata 700098 W Bengal India

Correct inference of genetic regulations inside a cell from the biological database like time series microarray data is one of the greatest challenges in post genomic era for biologists and researchers. Recurrent Neural Network (RNN) is one of the most popular and simple approach to model the dynamics as well as to infer correct dependencies among genes. Inspired by the behavior of social elephants, we propose a new metaheuristic namely Elephant Swarm Water Search Algorithm (ESWSA) to infer Gene Regulatory Network (GRN). This algorithm is mainly based on the water search strategy of intelligent and social elephants during drought, utilizing the different types of communication techniques. Initially, the algorithm is tested against benchmark small and medium scale artificial genetic networks without and with presence of diffrerent noise levels and the effciency was observed in term of parametric error, minimum fitness value, execution time, accuracy of prediction of true regulation, etc. Next, the proposed algorithm is tested against the real time gene expression data of Escherichia Coli SOS Network and results were also compared with others state of the art optimization methods. The experimental results suggest that ESWSA is very effcient for GRN inference problem and performs better than other methods in many ways.

关键词： Elephant Swarm Water Search Algorithm (ESWSA) gene expression gene regulatory network (GRN) metaheuristic microarray data optimization recurrent neural network (RNN)

来源：评论

学校读者我要写书评

暂无评论

Optimal False Discovery Rate Control with Kernel Density Estimation in a microarray Experiment

引用

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2016年第3期45卷 771-780页

作者： Kang, Moonsu Gangneung Wonju Natl Univ Dept Informat Stat Gangneung Si South Korea

Most of current false discovery rate (FDR) procedures in a microarray experiment assume restrictive dependence structures, resulting in being less reliable. FDR controlling procedure under suitable dependence structures based on Poisson distributional approximation is shown. Unlike other procedures, the distribution of false null hypotheses is estimated by using kernel density estimation allowing for dependent structures among the genes. Furthermore, we develop an FDR framework that minimizes the false nondiscovery rate (FNR) with a constraint on the controlled level of the FDR. The performance of the proposed FDR procedure is compared with that of other existing FDR controlling procedures, with an application to the microarray study of simulated data.

关键词： False discovery rate False nondiscovery rate Kernel density estimation microarray data Poisson approximation Primary 62 Secondary H15

来源：评论

学校读者我要写书评

暂无评论

The feature selection bias problem in relation to high-dimensional gene data

引用

ARTIFICIAL INTELLIGENCE IN MEDICINE 2016年第Jan.期66卷 63-71页

作者： Krawczuk, Jerzy Lukaszuk, Tomasz Bialystok Tech Univ Fac Comp Sci 45A Wiejska St PL-15351 Bialystok Poland

Objective: Feature selection is a technique widely used in data mining. The aim is to select the best subset of features relevant to the problem being considered. In this paper, we consider feature selection for the classification of gene datasets. Gene data is usually composed of just a few dozen objects described by thousands of features. For this kind of data, it is easy to find a model that fits the learning data. However, it is not easy to find one that will simultaneously evaluate new data equally well as learning data. This overfitting issue is well known as regards classification and regression, but it also applies to feature selection. Methods and materials: We address this problem and investigate its importance in an empirical study of four feature selection methods applied to seven high-dimensional gene datasets. We chose datasets that are well studied in the literature colon cancer, leukemia and breast cancer. All the datasets are characterized by a significant number of features and the presence of exactly two decision classes. The feature selection methods used are ReliefF, minimum redundancy maximum relevance, support vector machine recursive feature elimination and relaxed linear separability. Results: Our main result reveals the existence of positive feature selection bias in all 28 experiments (7 datasets and 4 feature selection methods). Bias was calculated as the difference between validation and test accuracies and ranges from 2.6% to as much as 41.67%. The validation accuracy (biased accuracy) was calculated on the same dataset on which the feature selection was performed. The test accuracy was calculated for data that was not used for feature selection (by so called external cross-validation). Conclusions: This work provides evidence that using the same dataset for feature selection and learning is not appropriate. We recommend using cross-validation for feature selection in order to reduce selection bias. (C) 2015 Elsevier B.V. All rights res

关键词： Feature selection bias Convex and piecewise linear classifier Support vector machine Gene selection microarray data

来源：评论

学校读者我要写书评

暂无评论

Orthogonal discriminant neighborhood analysis for tumor classification

引用

SOFT COMPUTING 2016年第1期20卷 263-271页

作者： Zhang, Chuanlei Lei, Ying-Ke Zhang, Shanwen Yang, Jucheng Hu, Yihua Tianjin Univ Sci & Technol Sch Comp Sci & Informat Engn Tianjin 300222 Peoples R China Inst Elect Engn Hefei 230027 Anhui Peoples R China Zhengzhou Univ Sias Int Univ Zhengzhou 451150 Peoples R China

An important application of gene expression data is tumor classification. Dimensionality reduction is a key step of tumor classification as gene expression data have the so-called large and small problem. To reduce the dimensionality of the microarray data, in this paper, a novel algorithm called orthogonal discriminant neighborhood analysis (ODNA) is proposed for tumor classification, which can reduce the effect resulting from over-fitting by pre-selecting a small subset of genes. Given a set of data points in the ambient space, a neighbor weight matrix is firstly built to describe the relationship among the data samples. Secondly, optimal between-class scatter matrix and within-class scatter matrix are defined such that the neighborhood structure can be preserved. To improve the discriminating ability, a new method is presented to orthogonalize the basis eigenvectors. The experimental results with two public microarray datasets demonstrate that the proposed ODNA is quite effective and feasible for tumor classification.

关键词： Locality sensitive discriminant analysis (LSDA) microarray data Orthogonal discriminant neighborhood analysis (ODNA) Tumor classification

来源：评论

学校读者我要写书评

暂无评论

Estimation of covariance and precision matrix, network structure, and a view toward systems biology

引用

WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL STATISTICS 2017年第6期9卷 e1415页

作者： Kuismin, Markku O. Sillanpaa, Mikko J. Univ Oulu Dept Math Sci Oulu Finland Univ Oulu Bioctr Oulu Oulu Finland

Covariance matrix and its inverse, known as the precision matrix, have many applications in multivariate analysis because their elements can exhibit the variance, correlation, covariance, and conditional independence between variables. The practice of estimating the precision matrix directly without involving any matrix inversion has obtained significant attention in the literature. We review the methods that have been implemented in R and their R packages, particularly when there are more variables than data samples and discuss ideas behind them. We describe how sparse precision matrix estimation methods can be used to infer network structure. Finally, we discuss methods that are suitable for gene coexpression network construction. (C) 2017 Wiley Periodicals, Inc.

关键词： Sparse precision matrix covariance matrix Gaussian graphical models network estimation microarray data shrinkage estimation

来源：评论

学校读者我要写书评

暂无评论

Shrinkage-based diagonal Hotelling's tests for high-dimensional small sample size data

引用

JOURNAL OF MULTIVARIATE ANALYSIS 2016年 143卷 127-142页

作者： Dong, Kai Pang, Herbert Tong, Tiejun Genton, Marc G. Hong Kong Baptist Univ Dept Math Hong Kong Hong Kong Peoples R China Univ Hong Kong Sch Publ Hlth Hong Kong Hong Kong Peoples R China Duke Univ Dept Biostat & Bioinformat Durham NC 27706 USA King Abdullah Univ Sci & Technol CEMSE Div Thuwal Saudi Arabia

High-throughput expression profiling techniques bring novel tools and also statistical challenges to genetic research. In addition to detecting differentially expressed genes, testing the significance of gene sets or pathway analysis has been recognized as an equally important problem. Owing to the "large p small n" paradigm, the traditional Hotelling's T-2 test suffers from the singularity problem and therefore is not valid in this setting. In this paper, we propose a shrinkage-based diagonal Hotelling's test for both one-sample and two-sample cases. We also suggest several different ways to derive the approximate null distribution under different scenarios of p and n for our proposed shrinkage-based test. Simulation studies show that the proposed method performs comparably to existing competitors when n is moderate or large, but it is better when n is small. In addition, we analyze four gene expression data sets and they demonstrate the advantage of our proposed shrinkage-based diagonal Hotelling's test. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Diagonal Hotelling's test High-dimensional data microarray data Null distribution Optimal variance estimation

来源：评论

学校读者我要写书评

暂无评论

Possible molecular mechanism underlying cadmium-induced circadian rhythms disruption in zebrafish

引用

BIOCHEMICAL AND BIOPHYSICAL RESEARCH COMMUNICATIONS 2016年第3-4期481卷 201-205页

作者： Xiao, Bo Chen, Tian-Ming Zhong, Yingbin Soochow Univ Coll Med Sch Biol & Basic Med Sci Suzhou 215123 Jiangsu Peoples R China Yancheng Inst Technol Sch Environm Sci & Engn Key Lab Ecol & Pollut Control Coastal Wetlands Yancheng 224051 Jiangsu Peoples R China

This study was aimed to explore the mechanisms underlying cadmium-induced circadian rhythms disruption. Two groups of zebrafish larvae treated with or without 5 ppm CdCl2 were incubated in a photoperiod of 14-h light/10-h dark conditions. The mRNA levels of clock1a, bmal1b, per2 and per1b in two groups were determined. microarray data were generated in two group of samples. Differential expression of genes were identified and the changes in expression level for some genes were validated by RT-PCR. Finally, Gene Ontology functional and KEGG pathway enrichment analysis of differentially expressed genes (DEGs) were performed. In comparison with normal group, the mRNA levels of clockla, bmal1b, and per2 were significantly changed and varied over the circadian cycle in CdCl2-treated group. DEGs were obtained from the light (84 h, ZT12) and dark (88 h, ZT16) phase. In addition, G-protein coupled receptor protein signaling pathway and immune response were both enriched by DEGs in both groups. While, proteolysis and amino acid metabolism were found associated with DEGs in light phase, and Neuroactive ligand-receptor interaction and oxidation-reduction process were significantly enriched by DEGs in dark phase. Besides, the expression pattern of genes including hsp701 and or115-11 obtained by RT-PCR were consistent with those obtained by microarray analysis. As a consequence, cadmium could make significant effects on circadian rhythms through immune response and G protein-coupled receptor signaling pathway. Besides, between the dark and the light phase, the mechanism by which cadmium inducing disruption of circadian rhythms were different to some extent. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Cadmium Circadian rhythms Zebrafish mRNA level microarray data

来源：评论

学校读者我要写书评

暂无评论

Gene expression analysis supports tumor threshold over 2.0 cm for T-category breast cancer

引用

EURASIP JOURNAL ON BIOINFORMATICS AND SYSTEMS BIOLOGY 2016年第1期2016卷 6页

作者： Solvang, Hiroko K. Frigessi, Arnoldo Kaveh, Fateme Riis, Margit L. H. Luders, Torben Bukholm, Ida R. K. Kristensen, Vessela N. Andreassen, Bettina K. Inst Marine Res Dept Marine Mammals C Sundts Gate 64 N-5004 Bergen Norway Univ Oslo Inst Basic Med Sci Dept Biostat Oslo Norway Stat Innovat Sfi 2 Oslo Norway Oslo Univ Hosp Ulleval Med Genet Dept Oslo Norway Akershus Univ Hosp Dept Surg Lorenskog Norway Akershus Univ Hosp Inst Clin Med Dept Mol Biol & Lab Sci EpiGen Lorenskog Norway Univ Oslo Norwegian Ctr HPH Network Inst Clin Med Oslo Norway Oslo Univ Hosp Radiumhosp Inst Canc Res Dept Genet Oslo Norway Canc Registry Norway Inst Populat Based Res Oslo Norway

Tumor size, as indicated by the T-category, is known as a strong prognostic indicator for breast cancer. It is common practice to distinguish the T1 and T2 groups at a tumor size of 2.0 cm. We investigated the 2.0-cm rule from a new point of view. Here, we try to find the optimal threshold based on the differences between the gene expression profiles of the T1 and T2 groups (as defined by the threshold). We developed a numerical algorithm to measure the overall differential gene expression between patients with smaller tumors and those with larger tumors among multiple expression datasets from different studies. We confirmed the performance of the proposed algorithm by a simulation study and then applied it to three different studies conducted at two Norwegian hospitals. We found that the maximum difference in gene expression is obtained at a threshold of 2.2-2.4 cm, and we confirmed that the optimum threshold was over 2.0 cm, as indicated by a validation study using five publicly available expression datasets. Furthermore, we observed a significant differentiation between the two threshold groups in terms of time to local recurrence for the Norwegian datasets. In addition, we performed an associated network and canonical pathway analyses for the genes differentially expressed between tumors below and above the given thresholds, 2.0 and 2.4 cm, using the Norwegian datasets. The associated network function illustrated a cellular assembly of the genes for the 2.0-cm threshold: an energy production for the 2.4-cm threshold and an enrichment in lipid metabolism based on the genes in the intersection for the 2.0- and 2.4-cm thresholds.

关键词： Breast cancer T-category Differentially expressed microarray data Two-group comparison statistical test Optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Extraction of essential features by quantum density

Extraction of essential features by quantum density

引用

Conference on Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments

作者： Wilinski, Artur Warsaw Univ Life Sci SGGW Nowoursynowska 159 PL-02776 Warsaw Poland

ISBN: (数字)9781510604865

ISBN: (纸本)9781510604858

In this paper we consider the problem of feature extraction, as an essential and important search of dataset. This problem describe the real ownership of the signals and images. Searches features are often difficult to identify because of data complexity and their redundancy. Here is shown a method of finding an essential features groups, according to the defined issues. To find the hidden attributes we use a special algorithm DQAL with the quantum density for the j-th features from original data, that indicates the important set of attributes. Finally, they have been generated small sets of attributes for subsets with different properties of features. They can be used to the construction of a small set of essential features. All figures were made in Matlab(6).

关键词： feature selection pattern recognition data reduction microarray data quantum density relevant set

来源：评论

学校读者我要写书评

暂无评论

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

Gene Selection Using Interaction Information for Microarray-...

引用

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

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：