Gene expression

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Gene expression is the process by which information from a gene is used in the synthesis of a functional gene product. (Wikipedia.org)






Conferences related to Gene expression

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2013 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)

The 2013 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS

  • 2012 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)

    Computational bioinformatics and systems biology communities due to the high modality of disparate high-throughput data, high variability of data acquisition, high dimensionality of biomedical data, and high complexity of genomics and proteomics. The theme of GENSIPS 12 is Methods in Next-Generation Sequencing and Cancer Systems Biology and GENSIPS will feature prominent plenary speakers including John Quackenbush of Harvard University, Victor Velculescu of Johns Hopkins University, Eberhard Voit of Georgia Tech, Jinghui Zhang of St. Jude Children's Research Hospital, as well as special sessions in cancer research.

  • 2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)

    To address the computational issues in the emerging field of computational genomics and proteomics, and to improve participation from not only within SP but computer science, statistics and biomedical communities. There are several advantages of hosting GENSIPS in San Antonio, which will help advance our goals.

  • 2010 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)

    Recent advances in genomic studies have stimulated synergistic research in many cross - disciplinary areas. Genomic data presents enormous challenges for signal processing and statistics, which has led to the development of the new field of Genomic Signal Processing (GSP). The seventh IEEE International Workshop on Genomic Signal Processing and Statistics will provide an international scientific forum devoted to the area of GSP and its applications in system biology and medicine.

  • 2009 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)

    Recent advances in genomic studies have stimulated synergistic research in many cross-disciplinary areas. Genomic data presents enormous challenges for signal processing and statistics, which has led to the development of the new field of Genomic Signal Processing (GSP). The seventh IEEE International Workshop on Genomic Signal Processing and Statistics will provide an international scientific forum devoted to the area of GSP and its applications in system biology and medicine. The aim of the workshop is to

  • 2008 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)

    Recent advances in genomic studies have stimulated synergistic research and development in many cross-disciplinary areas. Genomic data, especially the recent large-scale microarray gene expression data, present enormous challenges for signal processing and statistics. This challenge naturally is leading to a new field, genomic signal processing (GSP). This workshop addresses the emerging need for demonstrating to the signal processing community the potential for using signal-processing and statistical


2012 6th International Conference on Bioinformatics and Biomedical Engineering (iCBBE)

Bioinformatics, Computational Biology, Biomedical Engineering


2012 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

BIBM 2012 solicits high-quality original research papers (including significant work-in-progress) in any aspect of bioinformatics, biomedicine and healthcare informatics. New computational techniques and methods in machine learning; data mining; text analysis; pattern recognition; knowledge representation; databases; data modeling; combinatorics; stochastic modeling; string and graph algorithms; linguistic methods; robotics; constraint satisfaction; data visualization; parallel computation; data integration; modeling and simulation and their application in life science domain are especially encouraged.


2011 IEEE 5th International Conference on Nano/Molecular Medicine and Engineering (NANOMED)

1. Nano and molecular technologies in medical diagnosis and therapy 2. Nanotechnology in drug delivery 3. Biomedical imaging 4. Biochips and Bio-MEMS 5. Biomechatronics 6. Cell at the nanoscale 7. Biological interface 8. Frontiers in nanobiotechnology

  • 2010 IEEE 4th International Conference on Nano/Molecular Medicine and Engineering (NANOMED)

    The IEEE-NANOMED (IEEE International Conference on Nano/Molecular Medicine and Engineering) conference series is an annual conference organized by the IEEE Nanotechnology Council to bring together world-leading researchers focusing on the advancement of basic and clinical research in medical and biological sciences using engineering methods related to MEMS, Nano and Molecular technologies. The conference will deliver essential and advanced scientific and engineering information in the applications of MEMS/

  • 2009 IEEE 3rd International Conference on Nano/Molecular Medicine and Engineering (NANOMED)

    The conference aims to bring together world-leading researchers focusing on the advancement of basic and clinical research in medical and biological sciences using engineering methods related to MEMS, Nano and Molecular technologies. The conference will deliver essential and advanced scientific and engineering information in the applications of MEMS/NANO/Molecular technologies in medicine and biology to its participants. This conference aims to bring knowledge to all players in the field - academics from bo


2010 2nd International Conference on Computer Technology and Development (ICCTD)

The aim objective of ICCTD 2010 is to provide a platform for researchers, engineers, academicians as well as industrial professionals from all over the world to present their research results and development activities in Computer Technology and Development . This conference provides opportunities for the delegates to exchange new ideas and application experiences face to face, to establish business or research relations and to find global partners for future collaboration.


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Periodicals related to Gene expression

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Computational Biology and Bioinformatics, IEEE/ACM Transactions on

Specific topics of interest include, but are not limited to, sequence analysis, comparison and alignment methods; motif, gene and signal recognition; molecular evolution; phylogenetics and phylogenomics; determination or prediction of the structure of RNA and Protein in two and three dimensions; DNA twisting and folding; gene expression and gene regulatory networks; deduction of metabolic pathways; micro-array design and analysis; proteomics; ...


Nanobioscience, IEEE Transactions on

Basic and applied papers dealing both with engineering, physics, chemistry, and computer science and with biology and medicine with respect to bio-molecules and cells. The content of acceptable papers ranges from practical/clinical/environmental applications to formalized mathematical theory. TAB #73-June 2001. (Original name-IEEE Transactions on Molecular Cellular and Tissue Engineering). T-NB publishes basic and applied research papers dealing with the study ...




Xplore Articles related to Gene expression

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cDNA microarray analysis of human umbilical vein endothelial cells subjected to cyclic mechanical strain

S. R. Frye; S. G. Eskin; L. V. McIntire Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society] [Engineering in Medicine and Biology, 2002

Endothelial cells are constantly subjected to mechanical forces due to blood flow. The forces have been implicated in vascular pathogenesis; therefore, the consequences of these forces need to be examined. Using cDNA microarray technology, we studied the gene expression of human umbilical vein endothelial cells subjected to a 10% cyclic strain for 6 and 24 hours. Of the 4000 genes ...


Microarray analysis of the chondrocytic cell line T/C-28a2 under dynamic fluid shear

J. P. Abulencia; R. Gaspard; J. Quackenbush; K. Konstantopoulos Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society] [Engineering in Medicine and Biology, 2002

The behavior of the chondrocytic cell line T/C-28a2 under shear flow was examined using a 32,448 element microarray. A parallel plate flow chamber was used to generate a shear stress level of 20 dyn/cm2 for 1.5 or 24 hours (h), after which gene regulation was measured. Microarray analysis revealed differentially regulated genes affecting proliferation/differentiation, extracellular matrix/cytoskeleton, and inflammation at both ...


cGRNexp: a web platform for building combinatorial gene regulation networks based on user-uploaded gene expression datasets

Huayong Xu; Hui Yu; Kang Tu; Qianqian Shi; Chaochun Wei; Yuanyuan Li; Yixue Li 2012 IEEE 6th International Conference on Systems Biology (ISB), 2012

While we witness rapid progresses in development of methodologies/algorithms for constructing and analyzing the combinatorial regulation network which includes both TF regulators and miRNA regulators, we find a lack of tools or servers available for facilitating related works. A web service is especially needed that allows user to upload their own expression datasets and mine the combinatorial gene reglatory networks ...


Transductive support vector machines for classification of microarray gene expression data

R. Semolini; F. J. Von Zuben Proceedings of the International Joint Conference on Neural Networks, 2003., 2003

The purpose of this paper is to introduce transductive inference with support vector machines (TSVM) as a powerful methodology for classification of gene expression data, using training and prediction data sets. The following classification problems will be considered: determination of cancer diagnosis categories and classification of genes from the budding yeast Saccharomyces cerevisiae in functional groups. In the case of ...


Accurate genomic signal recovery using compressed sensing

Bakhtiyar Uddin; M. Emre Celebi; Hassan Kingravi; Gerald Schaefer Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), 2012

Microarrays are massively parallel biosensors that can simultaneously detect and quantify a large number of different genomic particles. A DNA microarray is a nucleic acid-based microarray that contains probe spots testing a multitude of targets in one experiment. Ideas from compressive sensing have been utilized in different ways in the analysis of DNA microarrays. One of the proposed methods is ...


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Educational Resources on Gene expression

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eLearning

cDNA microarray analysis of human umbilical vein endothelial cells subjected to cyclic mechanical strain

S. R. Frye; S. G. Eskin; L. V. McIntire Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society] [Engineering in Medicine and Biology, 2002

Endothelial cells are constantly subjected to mechanical forces due to blood flow. The forces have been implicated in vascular pathogenesis; therefore, the consequences of these forces need to be examined. Using cDNA microarray technology, we studied the gene expression of human umbilical vein endothelial cells subjected to a 10% cyclic strain for 6 and 24 hours. Of the 4000 genes ...


Microarray analysis of the chondrocytic cell line T/C-28a2 under dynamic fluid shear

J. P. Abulencia; R. Gaspard; J. Quackenbush; K. Konstantopoulos Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society] [Engineering in Medicine and Biology, 2002

The behavior of the chondrocytic cell line T/C-28a2 under shear flow was examined using a 32,448 element microarray. A parallel plate flow chamber was used to generate a shear stress level of 20 dyn/cm2 for 1.5 or 24 hours (h), after which gene regulation was measured. Microarray analysis revealed differentially regulated genes affecting proliferation/differentiation, extracellular matrix/cytoskeleton, and inflammation at both ...


cGRNexp: a web platform for building combinatorial gene regulation networks based on user-uploaded gene expression datasets

Huayong Xu; Hui Yu; Kang Tu; Qianqian Shi; Chaochun Wei; Yuanyuan Li; Yixue Li 2012 IEEE 6th International Conference on Systems Biology (ISB), 2012

While we witness rapid progresses in development of methodologies/algorithms for constructing and analyzing the combinatorial regulation network which includes both TF regulators and miRNA regulators, we find a lack of tools or servers available for facilitating related works. A web service is especially needed that allows user to upload their own expression datasets and mine the combinatorial gene reglatory networks ...


Transductive support vector machines for classification of microarray gene expression data

R. Semolini; F. J. Von Zuben Proceedings of the International Joint Conference on Neural Networks, 2003., 2003

The purpose of this paper is to introduce transductive inference with support vector machines (TSVM) as a powerful methodology for classification of gene expression data, using training and prediction data sets. The following classification problems will be considered: determination of cancer diagnosis categories and classification of genes from the budding yeast Saccharomyces cerevisiae in functional groups. In the case of ...


Accurate genomic signal recovery using compressed sensing

Bakhtiyar Uddin; M. Emre Celebi; Hassan Kingravi; Gerald Schaefer Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), 2012

Microarrays are massively parallel biosensors that can simultaneously detect and quantify a large number of different genomic particles. A DNA microarray is a nucleic acid-based microarray that contains probe spots testing a multitude of targets in one experiment. Ideas from compressive sensing have been utilized in different ways in the analysis of DNA microarrays. One of the proposed methods is ...


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IEEE.tv Videos

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IEEE-USA E-Books

  • Approaches to High-Dimensional Covariance and Precision Matrix Estimations

    This chapter introduces several recent developments for estimating large covariance and precision matrices without assuming the covariance matrix to be sparse. It explains two methods for covariance estimation: namely covariance estimation via factor analysis, and precision Matrix Estimation and Graphical Models. The low rank plus sparse representation holds on the population covariance matrix. The chapter presents several applications of these methods, including graph estimation for gene expression data, and several financial applications. It then shows how estimating covariance matrices of high- dimensional asset excess returns play a central role in applications of portfolio allocations and in risk management. The chapter explains the factor pricing model, which is one of the most fundamental results in finance. It elucidates estimating risks of large portfolios and large panel test of factor pricing models. The chapter illustrates the recent developments of efficient estimations in panel data models.

  • Analyzing Gene Expression Imaging Data in Developmental Biology

    This chapter describes the application of data-intensive methods to the automatic identification and annotation of gene expression patterns in the mouse embryo. The first section of the chapter introduces ideas behind modern computational and systems biology, how the explosion of data in the postgenomic world has led to new possibilities and even greater challenges. The second section talks about the particular computational biology problem and describes annotating images of gene expression with the right anatomical terms in depth. An automated solution based on data-intensive methods is discussed the third section. The final section looks ahead to the biological significance and systems biology application of these approaches and also describes a large-scale challenge and possible series of experiments with a novel data-intensive computational architecture.

  • Interpreting Microarray Data and Related Applications Using Nonlinear System Identification

    This chapter contains sections titled: Introduction Background Parallel Cascade Identification Constructing Class Predictors Prediction Based on Gene Expression Profiling Comparing Different Predictors Over the Same Data Set Concluding Remarks References

  • Microarray Data Analysis: General Concepts, Gene Selection, and Classification

    This chapter contains sections titled: Introduction From Microarray to Gene Expression Data Identification of Differentially Expressed Genes Classification: Unsupervised Methods Classification: Supervised Methods Conclusions

  • Biological Networks-Based Analysis of Gene Expression Signatures

    Biological networks, such as protein interaction networks and gene coexpression networks, are becoming popular in many studies, including the study of gene signatures of complex diseases. Computational biologists have developed many methods to identify gene signatures by combining gene expression profiles, biological networks, and other related data. Using data on biological networks, researchers also want to integrate different gene signatures by considering the interactions among genes. This chapter provides a brief introduction of gene signatures. Biological network-based identification of gene signatures is described here. Finally, the authors discuss protein interaction network-based integration of different gene signatures.

  • Classifying Gene Expression Profiles with Evolutionary Computation

    This chapter contains sections titled: DNA Microarray Data Classification Evolutionary Approach to the Problem Gene Selection with Speciated Genetic Algorithm Cancer Classification Based on Ensemble Genetic Programming Conclusion References

  • Analysis and Prediction of Protein Posttranslational Modification Sites

    Posttranslational modification (PTM) plays key roles in many cellular processes such as signaling, cellular differentiation, protein degradation, protein stability, gene expression regulation, protein function regulation, and protein interactions. It has been estimated that there are more than 200 types of PTMs mediated by enzymes consisting of 5% of the proteome. PTM site prediction is a binary classification problem. Musite framework was designed for PTM site prediction in three processes: (i) data collection and preparation, (ii) feature extraction, and (iii) classifier/prediction model training and evaluation. With its GNU GPL open-source license and extensible API, Musite provides an open framework for PTM site prediction applications. The Musite framework contains six core modules. The authors provide standalone and web server versions of Musite, both with intuitive graphical user interface (GUI) to access the prediction models that they trained.

  • Algorithmic Approaches to Clustering Gene Expression Data

    This chapter contains sections titled: Introduction, Biological Background, Mathematical Formulations and Background, Algorithms, Assessment of Solutions, A Case Study, Acknowledgments, References

  • Gene Regulation Bioinformatics of Microarray Data

    This chapter contains sections titled: Introduction Introduction to Transcriptional Regulation Measuring Gene Expression Profiles Preprocessing of Data Clustering of Gene Expression Profiles Cluster Validation Searching for Common Binding Sites of Coregulated Genes Inclusive: Online Integrated Analysis of Microarray Data Further Integrative Steps Conclusion References

  • Gene Expression Analysis: Joint Feature Selection and Classifier Design

    This chapter contains sections titled: Common Issues in Classifying Gene Expression Profiles, A Review of Feature Selection Methods for Kernel Machines, The Joint Classifier and Feature Optimization Algorithm, Experimental Studies Comparing the Methods, Discussion, Availability of Software, Appendix: Derivation for Q-Function and E-Step



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