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’13) will be held in Houston, TX during November 17-19, 2013. GENSIPS’13 will provide a forum for signal processing researchers, bioinformaticians, computational biologists, biomedical engineers, and biostatisticians to exchange ideas and discuss the challenges confronting 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.

  • 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.



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 ...



Most published Xplore authors for Gene expression

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Xplore Articles related to Gene expression

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Scale-Specific Similarity Measure for Analysis of Gene Expression Time Series

Li Ying 2009 3rd International Conference on Bioinformatics and Biomedical Engineering, 2009

Combined cross-correlation and multi-resolution of maximal overlap discrete wavelets, the scale-specific similarity measure for the analysis of gene expression time series is provided, which can capture the relationship of co- expression under time-delay and local time points. The scale-specific similarity measure have more possible to capture more biological knowledge than Pearson correlation and cross correlation.


REPA: Applying Pathway Analysis to Genome-wide Transcription Factor Binding Data

Pranjal Patra; Tatsuo Izawa; Lourdes Pena Castillo IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2016

Pathway analysis has been extensively applied to aid in the interpretation of the results of genome-wide transcription profiling studies, and has been shown to successfully find associations between the biological phenomena under study and biological pathways. There are two widely used approaches of pathway analysis: over-representation analysis, and gene set analysis. Recently genome-wide transcription factor binding data has become widely ...


Identifying microRNA and gene expression networks using graph communities

Benika Hall; Andrew Quitadamo; Xinghua Shi Tsinghua Science and Technology, 2016

Integrative network analysis is powerful in helping understand the underlying mechanisms of genetic and epigenetic perturbations for disease studies. Although it becomes clear that microRNAs, one type of epigenetic factors, have direct effect on target genes, it is unclear how microRNAs perturb downstream genetic neighborhood. Hence, we propose a network community approach to integrate microRNA and gene expression profiles, to ...


A novel combined ICA and clustering technique for the classification of gene expression data

A. Kapoor; T. Bowles; J. Chambers Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005., 2005

This study presents an effective method of blindly classifying large amounts of gene expression data into biologically meaningful groups using a combination of independent component analysis (ICA) and clustering techniques. Specifically, we show that the genes can be classified blindly into several groups based solely on their expression profiles. These groups have a very close correspondence with benchmarks obtained by ...


Treelets as feature transformation tool for block diagonal linear discrimination

Lingyan Sheng; Antonio Ortega; Roger Pique-Regi; Shahab Asgharzadeh 2009 IEEE International Workshop on Genomic Signal Processing and Statistics, 2009

The main novelty of this paper is to apply treelets as a feature transformation tool prior to using block diagonal linear discriminant analysis (BDLDA). Using pairwise feature transformations, treelets seek to approximate the decorrelation behavior of principal component analysis (PCA) without requiring identifying the eigenvectors of the full covariance matrix. Instead, PCA is successively applied to pairs of features, which ...


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Educational Resources on Gene expression

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eLearning

Scale-Specific Similarity Measure for Analysis of Gene Expression Time Series

Li Ying 2009 3rd International Conference on Bioinformatics and Biomedical Engineering, 2009

Combined cross-correlation and multi-resolution of maximal overlap discrete wavelets, the scale-specific similarity measure for the analysis of gene expression time series is provided, which can capture the relationship of co- expression under time-delay and local time points. The scale-specific similarity measure have more possible to capture more biological knowledge than Pearson correlation and cross correlation.


REPA: Applying Pathway Analysis to Genome-wide Transcription Factor Binding Data

Pranjal Patra; Tatsuo Izawa; Lourdes Pena Castillo IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2016

Pathway analysis has been extensively applied to aid in the interpretation of the results of genome-wide transcription profiling studies, and has been shown to successfully find associations between the biological phenomena under study and biological pathways. There are two widely used approaches of pathway analysis: over-representation analysis, and gene set analysis. Recently genome-wide transcription factor binding data has become widely ...


Identifying microRNA and gene expression networks using graph communities

Benika Hall; Andrew Quitadamo; Xinghua Shi Tsinghua Science and Technology, 2016

Integrative network analysis is powerful in helping understand the underlying mechanisms of genetic and epigenetic perturbations for disease studies. Although it becomes clear that microRNAs, one type of epigenetic factors, have direct effect on target genes, it is unclear how microRNAs perturb downstream genetic neighborhood. Hence, we propose a network community approach to integrate microRNA and gene expression profiles, to ...


A novel combined ICA and clustering technique for the classification of gene expression data

A. Kapoor; T. Bowles; J. Chambers Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005., 2005

This study presents an effective method of blindly classifying large amounts of gene expression data into biologically meaningful groups using a combination of independent component analysis (ICA) and clustering techniques. Specifically, we show that the genes can be classified blindly into several groups based solely on their expression profiles. These groups have a very close correspondence with benchmarks obtained by ...


Treelets as feature transformation tool for block diagonal linear discrimination

Lingyan Sheng; Antonio Ortega; Roger Pique-Regi; Shahab Asgharzadeh 2009 IEEE International Workshop on Genomic Signal Processing and Statistics, 2009

The main novelty of this paper is to apply treelets as a feature transformation tool prior to using block diagonal linear discriminant analysis (BDLDA). Using pairwise feature transformations, treelets seek to approximate the decorrelation behavior of principal component analysis (PCA) without requiring identifying the eigenvectors of the full covariance matrix. Instead, PCA is successively applied to pairs of features, which ...


More eLearning Resources

IEEE-USA E-Books

  • References

    An unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules. The demands and opportunities for interpreting these data are expanding rapidly. Bioinformatics is the development and application of computer methods for management, analysis, interpretation, and prediction, as well as for the design of experiments. Machine learning approaches (e.g., neural networks, hidden Markov models, and belief networks) are ideally suited for areas where there is a lot of data but little theory, which is the situation in molecular biology. The goal in machine learning is to extract useful information from a body of data by building good probabilistic models--and to automate the process as much as possible.In this book Pierre Baldi and Sÿren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed both at biologists and biochemists who need to understand new data-driven algorithms and at those with a primary background in physics, mathematics, statistics, or computer science who need to know more about applications in molecular biology.This new second edition contains expanded coverage of probabilistic graphical models and of the applications of neural networks, as well as a new chapter on microarrays and gene expression. The entire text has been extensively revised.

  • A Functional Model of Cell Genome

    This paper is concerned with a model of the cell genome called Artificial Genome, that tries to model some aspects of the cell cycle, in particular those related to gene expression, cell differentiation and cell growth. The functioning of the model during interphase and mitosis is explained in detail through an example, that shows how the four functional categories of the Artificial Genome (Functions, Code, Data and Buffer) interact to determine the phenotype. The capacity of the model of generating phenotypical patterns, represented as 2-dimensional shapes, is explored through a simulation, that evolves in 9 cycles a cell to become a small face made up of 132 cells. Finally some parlallels between the Artificial Genome and the natural one are discussed.

  • Hybrid of Neural Classifier and Swarm Intelligence in Multiclass Cancer Diagnosis with Gene Expression Signatures

    This chapter contains sections titled: Introduction Methods and Systems Experimental Results Conclusions References

  • Index

    An unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules. The demands and opportunities for interpreting these data are expanding rapidly. Bioinformatics is the development and application of computer methods for management, analysis, interpretation, and prediction, as well as for the design of experiments. Machine learning approaches (e.g., neural networks, hidden Markov models, and belief networks) are ideally suited for areas where there is a lot of data but little theory, which is the situation in molecular biology. The goal in machine learning is to extract useful information from a body of data by building good probabilistic models--and to automate the process as much as possible.In this book Pierre Baldi and Sÿren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed both at biologists and biochemists who need to understand new data-driven algorithms and at those with a primary background in physics, mathematics, statistics, or computer science who need to know more about applications in molecular biology.This new second edition contains expanded coverage of probabilistic graphical models and of the applications of neural networks, as well as a new chapter on microarrays and gene expression. The entire text has been extensively revised.

  • 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

  • Stochastic Modeling of Intracellular Kinetics

    This chapter contains section titled: 8.1 Chapter Overview, 8.2 Basic Models for Stochastic Kinetics, 8.3 Stochastic Gene Expression, 8.4 Fluctuation- Dissipation Approximations, 8.5 Fluctuations near Critical Points, 8.6 Negative Feedback of Replication Control, 8.7 Noise Induced Transitions, Acknowledgements, Notes

  • Finding Clusters in Gene Expression Data Using EvoCluster

    This chapter contains sections titled: Introduction Related Work Evolutionary Clustering Algorithm Experimental Results Conclusions References

  • Support Vector Machine Applications in Computational Biology

    This chapter contains sections titled: Introduction, Protein Remote Homology Detection, Protein Remote Homology Detection, Prediction along the DNA or Protein Strand, Microarray Gene Expression Analysis, Data Fusion, Other Applications, Discussion

  • 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.

  • 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



Standards related to Gene expression

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