检索结果-内蒙古大学图书馆

Identification of genes associated with methotrexate resistance in methotrexate-resistant osteosarcoma cell lines

JOURNAL OF ORTHOPAEDIC SURGERY AND RESEARCH 2015年第1期10卷 1-9页

作者： Yang, Xiao-rong Xiong, Yan Duan, Hong Gong, Ren-rong Sichuan Univ West China Hosp Dept Operat Room Chengdu 610041 Sichuan Peoples R China Sichuan Univ West China Hosp Dept Orthoped Chengdu 610041 Sichuan Peoples R China

Background: This study aimed to better understand the mechanisms underlying methotrexate (MTX)-resistance in osteosarcoma. Methods: The raw transcription microarray data GSE16089 collected from three MTX-sensitive osteosarcoma (Saos-2) cell samples and three MTX-resistant osteosarcoma (Saos-2) cell samples were downloaded from Gene Expression Omnibus. After data processing, the differentially expressed genes (DEGs) were identified. Next, DEGs were submitted to DAVID for functional annotation based on the GO (Gene Ontology) database, as well as pathway enrichment analysis based on the KEGG (Kyoto Encyclopedia of Genes and Genomes) database. Transcription factors (TFs) and tumor-associated genes (TAGs) were identified with reference to TRANSFAC and TAG, and TSGene databases, respectively. The protein-protein interaction (PPI) network of the gene-encoded products was constructed, and the subnetwork with the highest score was also detected using Search Tool for the Retrieval of Interacting Genes and BioNet package. Results: A total of 690 up-regulated genes and down-regulated 626 genes were identified. Up-regulated DEGs (including AARS and PARS2) were associated to transfer RNA (tRNA) aminoacylation while down-regulated DEGs (including AURKA, CCNB1, CCNE2, CDK1, and CENPA) were correlated with mitotic cell cycle. Totally, 13 TFs (including HMGB2), 13 oncogenes (including CCNA2 and AURKA), and 19 tumor suppressor genes (TSGs) (including CDKN2C) were identified from the down-regulated DEGs. Ten DEGs, including nine down-regulated genes (such as AURKA, CDK1, CCNE2, and CENPA) and one up-regulated gene (GADD45A), were involved in the highest score subnetwork. Conclusion: AARS, AURKA, AURKB, CENPA, CCNB1, CCNE2, and CDK may contribute to MTX resistance via aminoacyl-tRNA biosynthesis pathway, cell cycle pathway, or p53 signaling pathway.

关键词： Osteosarcoma Methotrexate resistance microarray data

来源：评论

学校读者我要写书评

暂无评论

TMA-TAB: A spreadsheet-based document for exchange of tissue microarray data based on the tissue microarray-object model

引用

JOURNAL OF BIOMEDICAL INFORMATICS 2010年第3期43卷 435-441页

作者： Song, Young Soo Lee, Hye Won Park, Yu Rang Kim, Do Kyoon Sim, Jaehyun Kang, Hyunseok Peter Kim, Ju Han Seoul Natl Univ Coll Med Div Biomed Informat Seoul 110799 South Korea Seoul Natl Univ Coll Med SNUBI Seoul 110799 South Korea Univ Florida Coll Med Dept Mol Genet & Microbiol Gainesville FL 32611 USA Roswell Pk Canc Inst Dept Pathol & Lab Med Buffalo NY 14263 USA

The importance of tissue microarrays (TMA) as clinical validation tools for cDNA microarray results is increasing, whereas researchers are still suffering from TMA data management issues. After we developed a comprehensive data model for TMA data storage, exchange and analysis, TMA-OM, we focused our attention on the development of a user-friendly exchange format with high expressivity in order to promote data communication of TMA results and TMA-OM supportive database applications. We developed TMA-TAB, a spreadsheet-based data format for TMA data submission to the TMA-OM supportive TMA database system. TMA-TAB was developed by simplifying, modifying and reorganizing classes, attributes and templates of TMA-OM into five entities: experiment, block, slide, core_in_block, and core_in_slide. Five tab-delimited formats (investigation design format, block description format, slide description format, core clinicohistopathological data format, and core result data format) were made, each representing the entities of experiment, block, slide, core_in_block, and core_in_slide. We implemented TMA-TAB import and export modules on Xperanto-TMA, a TMA-OM supportive database application, to facilitate data submission. Development and implementation of TMA-TAB and TMA-OM provide a strong infrastructure for powerful and user-friendly TMA data management. (C) 2009 Elsevier Inc. All rights reserved.

关键词： database microarray data Modeling Tissue microarray

来源：评论

学校读者我要写书评

暂无评论

Predicting incomplete gene microarray data with the use of supervised learning algorithms

引用

PATTERN RECOGNITION LETTERS 2010年第13期31卷 2061-2069页

作者： Twala, Bhekisipho Phorah, Motee Univ Johannesburg Fac Engn & Built Environm Dept Elect & Elect Engn Sci ZA-2006 Johannesburg South Africa CSIR Digital Intelligence Res Grp ZA-0001 Pretoria South Africa

Motivation: With the wealth of sequence data and the huge amount of data generated from molecular technologies, the issue of gene classification/prediction has become a central challenge in the field of microarray data analysis. This has led to the application of many well-established supervised learning (SL) algorithms in an attempt to provide more accurate and automatic diagnosis class (cancer/non cancer) prediction. Virtually all research on SL addresses the task of learning to classify complete domain instances. However, in some research situations we often have to classify instances given incomplete vectors, which can affect the predictive accuracy of learned classifiers. The task of learning an accurate incomplete data classifier from instances raises a number of new issues some of which have not been properly addressed by bioinformatics research. Thus, an effective missing value estimation method is required for improving predictive accuracy. Results: The essence of the approach is the proposal that prediction using supervised learning can be improved in probabilistic terms given incomplete microarray data. This imputation approach is based on the a priori probability of each value determined from the instances at that node of a decision tree (PDT) that have specified values. The proposed approach exploits the total probability and Bayes' theorems and it has three versions. We evaluate our approach with other supervised learning techniques including C5.0, classification and regression trees (CART), k-nearest neighbour (k-NN), linear discrimination (LD) naive Bayes classifier (NBC), Repeated Incremental Pruning to Produce Error Reduction (RIPPER) and support vector machines (SVMs), from the point of view of their effect or tolerance of incomplete test data. Eight cancer related gene expression datasets are utilized for this task. Experimental results are provided to illustrate the efficiency and the robustness of the proposed algorithm. (c) 2010 Published by E

关键词： Supervised learning microarray data Incomplete data Prediction

来源：评论

学校读者我要写书评

暂无评论

Protein-protein interaction network analysis and identifying regulation microRNAs in asthmatic children

引用

ALLERGOLOGIA ET IMMUNOPATHOLOGIA 2015年第6期43卷 584-592页

作者： Chen, Y. Qiao, J. Shanghai Jiao Tong Univ Sch Med Shanghai Peoples Hosp 1 Dept Resp Med Shanghai 200080 Peoples R China Shanghai Jiao Tong Univ Sch Med Shanghai Peoples Hosp 9 Dept Resp Med Shanghai 200011 Peoples R China

Background: Asthma is a chronic inflammatory airway disease, the incidence of which has increased recently. In order to identify the potential biomarkers for allergic asthma therapy, microarray data were analysed to find meaningful information. Methods: microarray data GSE18965 were downloaded from Gene Expression Ominibus (GEO), including seven asthmatic epithelium samples from children with allergic asthma and nine healthy controls. Limma package was used to detect differentially expressed genes (DEGs) and the criteria were vertical bar log fold change vertical bar >0.5 and p value < 0.05. We used database for Annotation, Visualization and Integrated Discovery (DAVID) tool to perform GO function and KEGG pathway analysis. STRING database was used to construct protein-protein interaction (PPI) network. MicroRNA (miRNA) regulation network was constructed according to miRecords database. Results: We identified 274 DEGs in asthma epithelium samples comparing with healthy controls. There were 123 up-regulated DEGs and 151 down-regulated DEGs. PPI network analysis showed that TSPO, G6PD and TXN had higher degree. miRNA regulation network demonstrated that miR-16 and miR-15a had higher degree. The target genes of miRNAs were significantly enriched in the apoptosis function. Conclusions: TSPO, G6PD and TXN, miR-16, miR-15a and apoptosis may be used as the targets for children's allergic asthma therapy. (C) 2014 SEICAP. Published by Elsevier Espana, S.L.U. All rights reserved.

关键词： Asthmatic epithelium microarray data Differentially expressed genes Protein-protein interaction network MicroRNA

来源：评论

学校读者我要写书评

暂无评论

Computational Approach to Detect the Culprit Genes Responsible for a Disease

Computational Approach to Detect the Culprit Genes Responsib...

引用

International Symposium on Advanced Computing and Communication (ISACC)

作者： War, Daphne Kordor Saha, Goutam North Eastern Hill Univ NEHU IT Dept Shillong Meghalaya India

ISBN: (纸本)9781467367080

Computational exploration of the identity of genes which may be responsible for a particular disease is a very exhaustive study nowadays. This uses the datasets available in different websites which store a tremendous amount of genetic information in the form of microarray data. In this paper, we have presented a reliable and effective approach for unveiling the smallest possible set of genes which is associated with even a quite poor prognosis disease like Alzheimer Disease. The dataset used here has been collected from the official website of NCBI. We have used rough set theory, random forest, and principal component analysis or their collective form for the said purpose. The maximum accuracy achievable here for the purpose of diagnosis is quite satisfactory. Further, we have verified our result from DAVID ontological website where it has been found that most of the genes extracted computationally are really associated with Alzheimer's disease.

关键词： Alzheimer's disease data mining microarray data principal component analysis randomforest rough set theory

来源：评论

学校读者我要写书评

暂无评论

Inferring gene regulatory networks from asynchronous microarray data with AIRnet

引用

BMC GENOMICS 2010年第2期11卷 1-8页

作者： Oviatt, David Clement, Mark Snell, Quinn Sundberg, Kenneth Lai, Chun Wan J. Allen, Jared Roper, Randall Brigham Young Univ Dept Comp Sci Provo UT 84602 USA Brigham Young Univ Dept Chem & Biochem Provo UT 84602 USA Indiana Univ Purdue Univ Dept Biol Indianapolis IN 46205 USA

Background: Modern approaches to treating genetic disorders, cancers and even epidemics rely on a detailed understanding of the underlying gene signaling network. Previous work has used time series microarray data to infer gene signaling networks given a large number of accurate time series samples. microarray data available for many biological experiments is limited to a small number of arrays with little or no time series guarantees. When several samples are averaged to examine differences in mean value between a diseased and normal state, information from individual samples that could indicate a gene relationship can be lost. Results: Asynchronous Inference of Regulatory Networks (AIRnet) provides gene signaling network inference using more practical assumptions about the microarray data. By learning correlation patterns for the changes in microarray values from all pairs of samples, accurate network reconstructions can be performed with data that is normally available in microarray experiments. Conclusions: By focussing on the changes between microarray samples, instead of absolute values, increased information can be gleaned from expression data.

关键词： microarray data Regulatory Network Gene Regulatory Network microarray Sample Network Component Analysis

来源：评论

学校读者我要写书评

暂无评论

Neural Network Based Gene Regulatory Network Reconstruction 3

Neural Network Based Gene Regulatory Network Reconstruction

引用

3rd International Conference on Computer, Communication, Control and Information Technology (C3IT)

作者： Mandal, Sudip Saha, Goutam Pal, Rajat Kumar GIMT ECE Dept Krishna Nagar India NEHU IT Dept Shillong Meghalaya India Univ Calcutta CSE Dept Kolkata India

ISBN: (纸本)9781479944453

Gene Regulatory Networks (GRN) is used to model the regulations in living organisms. Inferring genetic network from different experimental high throughput biological data (like microarray) is a challenging job for all researchers. In this paper, Artificial Neural Network, which is a very effective soft computing tool to learn and model the dynamics or dependencies between genes, is used for reconstruction of small scale GRN from the reduced microarray dataset of Lung Adenocarcinoma. The significances of regulations of one gene to other genes of the system are expressed by a weight matrix which is computed using Perceptron based biologically significant weight updating method by minimizing the error during learning. Based on the values of elements of filtered weight matrix, a directed weighted graph can be drawn successfully that denotes gene regulatory network.

关键词： Gene Regulatory Network microarray data Neural Network

来源：评论

学校读者我要写书评

暂无评论

S-System Based Gene Regulatory Network Reconstruction Using Firefly Algorithm 3

S-System Based Gene Regulatory Network Reconstruction Using ...

引用

3rd International Conference on Computer, Communication, Control and Information Technology (C3IT)

作者： Mandal, Sudip Saha, Goutam Pal, Rajat K. GIMT ECE Dept Krishna Nagar India NEHU IT Dept Shillong Meghalaya India Univ Calcutta CSE Dept Kolkata India

ISBN: (纸本)9781479944453

The correct inference of gene regulatory network plays a critical role in understanding biological regulation in cells and genome based therapeutics. DNA microarray is the most widely used technology for extracting the relationships between thousands of genes simultaneously. Since S-system is based on the rate law, it is considered as a suitable mathematical model for representing complex biological reactions between genes. As, this problem has multiple solutions, optimized solution need to be identified via different nature inspired metaheuristic algorithms. So, in this paper, a new method is elaborated that helps to infer gene regulatory network for Lung Adenocarcinoma using S-system and Firefly Optimization which is an efficient but simple metaheuristic inspired by natural motion of fireflies. By optimizing the values of parameters of the S-system model, gene network can be easily reconstructed and inferred. Though direct biological validation of the network is not possible, but accuracy of the proposed method can be described as how well the network fit with the initial training data for different genes which is quite satisfactory for our research work.

关键词： Gene Regulatory Network microarray data S-stem Firefly Algorithm

来源：评论

学校读者我要写书评

暂无评论

Probabilistic Graphical Models and Deep Belief Networks for Prognosis of Breast Cancer 14

Probabilistic Graphical Models and Deep Belief Networks for ...

引用

IEEE 14th International Conference on Machine Learning and Applications ICMLA

作者： Khademi, Mahmoud Nedialkov, Nedialko S. McMaster Univ Dept Comp & Software Hamilton ON Canada

ISBN: (纸本)9781509002870

We propose a probabilistic graphical model (PGM) for prognosis and diagnosis of breast cancer. PGMs are suitable for building predictive models in medical applications, as they are powerful tools for making decisions under uncertainty from big data with missing attributes and noisy evidence. Previous work relied mostly on clinical data to create a predictive model. Moreover, practical knowledge of an expert was needed to build the structure of a model, which may not be accurate. In our opinion, since cancer is basically a genetic disease, the integration of microarray and clinical data can improve the accuracy of a predictive model. However, since microarray data is high-dimensional, including genomic variables may lead to poor results for structure and parameter learning due to the curse of dimensionality and small sample size problems. We address these problems by applying manifold learning and a deep belief network (DBN) to microarray data. First, we construct a PGM and a DBN using clinical and microarray data, and extract the structure of the clinical model automatically by applying a structure learning algorithm to the clinical data. Then, we integrate these two models using softmax nodes. Extensive experiments using real-world databases, such as METABRIC and NKI, show promising results in comparison to Support Vector Machines (SVMs) and k-Nearest Neighbors (k-NN) classifiers, for classifying tumors and predicting events like recurrence and metastasis.

关键词： probabilistic graphical models deep belief networks breast cancer microarray data

来源：评论

学校读者我要写书评

暂无评论

Detection of Differentially Expressed Genes in Wild Type HIV-1 Vpr and Two HIV-1 Mutant Vprs 3

Detection of Differentially Expressed Genes in Wild Type HIV...

引用

3rd International Conference on Frontiers in Intelligent Computing - Theory and Applications (FICTA)

作者： Barman, Bandana Mukhopadhyay, Anirban Kalyani Govt Engn Coll Dept Elect & Commun Engn Nadia W Bengal India Univ Kalyani Dept Comp Sci & Engn Nadia W Bengal India

ISBN: (纸本)9783319119328

Identification of differentially expressed genes between two sample groups are important to find which genes are increased in expression (up-regulated) or decreased in expression (down-regulated). We have identified differentially expressed genes between wild type HIV-1 Vpr and two HIV-1 mutant Vprs separately by using statistical t-test and false discovery rate. We also compute q-value of test to measure minimum FDR which occurs. We have found 1524 number of differentially expressed genes between wild type HIV-1 vpr and HIV-1 mutant vpr, R80A. Again we found 1525 diffrential genes between wild type HIV1 vpr and HIV-1 mutant vpr, F72A/R73A. From these two differentially expressed gene sets we get 941 number of down-regulated genes for both sets and rest genes are found as up-regulated genes.

关键词： microarray data p-value false discovery data q-value normalization permutation test differential gene expression

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：