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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Functional data classification for temporal gene expression data with kernel-induced random forests

Guangzhe Fan; Jiguo Cao; Jiheng Wang 2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, 2010

Scientists and others today often collect samples of curves and other functional data. The multivariate data classification methods cannot be directly used for functional data classification because the curse of dimensionality and difficulty in taking in account the correlation and order of functional data. We extend the kernel-induced random forest method for discriminating functional data by defining kernel functions of ...


Segmenting Microarray Image Spots using an Active Contour Approach

Jinn Ho; Wen-Liang Hwang 2007 IEEE International Conference on Image Processing, 2007

Inspired by Paragious and Deriche's work, which unifies boundary-based and region-based image partition approaches, we integrate the active contour(snake) model and the Fisher criterion to capture, respectively, the boundary and region information of microarray images. We then use the proposed algorithm to automatically segment the spots in the microarray images, and compare our results with those obtained by commercial software.


An Application Platform Enabling High Performance Grid Processing of Microarray Experiments

Ilias Maglogiannis; Aristotelis Chatzioannou; John Soldatos; Vasileios Mylonakis; Yiannis Kanaris Twentieth IEEE International Symposium on Computer-Based Medical Systems (CBMS'07), 2007

Reliable microarray experiments are extremely computationally demanding given the need to perform complex calculations over a multitude of data. Grid computing can provide a remedy to this problem through accelerating the computations associated with microarray normalization, while at the same time providing access to vast amounts of federated storage resources. The present paper presents the application architecture for the HECTOR ...


Connectionist Modelling of Dynamics of Gene Expression and Reverse Engineering Gene Regulatory Networks

R. K. De; K. Biswas The 2006 IEEE International Joint Conference on Neural Network Proceedings, 2006

In this article we develop two connectionist models describing the dynamics of gene expression incorporating protein concentration. The models are based on the theoretical study of Goutsias and Kim. We calculate the concentration of mRNAs and proteins at different time steps, and the concentrations of mRNAs and proteins are calculated as a function of step n. Here we consider concentration ...


Mutual information based reduction of data mining dimensionality in gene expression analysis

V. Marohnic; Z. Debeljak; N. Bogunovic 26th International Conference on Information Technology Interfaces, 2004., 2004

This article introduces a novel method for reducing dimensional complexity of classification problems which are frequently present in gene microarray analysis. Revealing the most relevant subset of genes among few thousands of analyzed genes is necessary to get accurate disease classification. Attribute (gene) filter was developed for such a purpose. The filter, first introduced as mutual information feature selection (MIPS) ...


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Educational Resources on Gene expression

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eLearning

Functional data classification for temporal gene expression data with kernel-induced random forests

Guangzhe Fan; Jiguo Cao; Jiheng Wang 2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, 2010

Scientists and others today often collect samples of curves and other functional data. The multivariate data classification methods cannot be directly used for functional data classification because the curse of dimensionality and difficulty in taking in account the correlation and order of functional data. We extend the kernel-induced random forest method for discriminating functional data by defining kernel functions of ...


Segmenting Microarray Image Spots using an Active Contour Approach

Jinn Ho; Wen-Liang Hwang 2007 IEEE International Conference on Image Processing, 2007

Inspired by Paragious and Deriche's work, which unifies boundary-based and region-based image partition approaches, we integrate the active contour(snake) model and the Fisher criterion to capture, respectively, the boundary and region information of microarray images. We then use the proposed algorithm to automatically segment the spots in the microarray images, and compare our results with those obtained by commercial software.


An Application Platform Enabling High Performance Grid Processing of Microarray Experiments

Ilias Maglogiannis; Aristotelis Chatzioannou; John Soldatos; Vasileios Mylonakis; Yiannis Kanaris Twentieth IEEE International Symposium on Computer-Based Medical Systems (CBMS'07), 2007

Reliable microarray experiments are extremely computationally demanding given the need to perform complex calculations over a multitude of data. Grid computing can provide a remedy to this problem through accelerating the computations associated with microarray normalization, while at the same time providing access to vast amounts of federated storage resources. The present paper presents the application architecture for the HECTOR ...


Connectionist Modelling of Dynamics of Gene Expression and Reverse Engineering Gene Regulatory Networks

R. K. De; K. Biswas The 2006 IEEE International Joint Conference on Neural Network Proceedings, 2006

In this article we develop two connectionist models describing the dynamics of gene expression incorporating protein concentration. The models are based on the theoretical study of Goutsias and Kim. We calculate the concentration of mRNAs and proteins at different time steps, and the concentrations of mRNAs and proteins are calculated as a function of step n. Here we consider concentration ...


Mutual information based reduction of data mining dimensionality in gene expression analysis

V. Marohnic; Z. Debeljak; N. Bogunovic 26th International Conference on Information Technology Interfaces, 2004., 2004

This article introduces a novel method for reducing dimensional complexity of classification problems which are frequently present in gene microarray analysis. Revealing the most relevant subset of genes among few thousands of analyzed genes is necessary to get accurate disease classification. Attribute (gene) filter was developed for such a purpose. The filter, first introduced as mutual information feature selection (MIPS) ...


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IEEE-USA E-Books

  • Genetic Mechanisms Underlying the Baldwin Effect Are Evident in Natural Antibodies

    J.M. Baldwin theorized that individual learning allows an organism to exploit genetic variations that only partially determine a physiological structure; consequently, useful genetic variations are increased in subsequent generations. Theoretical studies have demonstrated the Baldwin effect in the evolution of simulated neural networks and in quantitative genetics models. Heretofore, it has not been shown how the Baldwin effect is manifested in the genetics of a specific biological system. The adaptive antibody population has all the requisite elements of a learning system, and as theorized by Baldwin, this ability to learn can facilitate the evolution of other structures. I show how the adaptive antibody population facilitates the evolution of a separate, genetically-determinant antibody population, responsible for natural antibodies. Both natural and adaptive antibodies are constructed from a common library of gene segments. Natural antibodies, however, express a restricted set of the gene segment libraries. There are two competing theories for the mechanisms of biased gene usage: the "proximal usage" hypothesis and "preferential expression" hypothesis. The genetic manifestation of the Baldwin effect in the 1g genes depends on the mechanism of gene expression. Under the proximal usage hypothesis, the Baldwin effect is a direct consequence of quantitative genetics. Under the preferential expression hypothesis, gene segments in the parental genes can become marked for preferential expression in the natural antibody population of their offspring. This system is ideal for experimental and modeling studies of the interactions between learning and evolution.

  • 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.

  • Evaluating an Evolutionary Approach for Reconstructing Gene Regulatory Networks

    Reconstructing networks from (partial) incomplete data is a general problem in biology. We use an evolutionary approach in an artificial network creation and reconstruction framework to investigate limitations of gene expression network inference from simulated microarray data. For this, the simulated dynamics of the evolved networks are optimized to fit the target dynamics. Evolving networks with similar dynamics is not as difficult as comparing the resulting network topologies to the original network to be reconstructed.

  • Modelling transcriptional regulation using Gaussian Processes

    Modelling the dynamics of transcriptional processes in the cell requires the knowledge of a number of key biological quantities. While some of them are relatively easy to measure, such as mRNA decay rates and mRNA abundance levels, it is still very hard to measure the active concentration levels of the transcription factor proteins that drive the process and the sensitivity of target genes to these concentrations. In this paper we show how these quantities for a given transcription factor can be inferred from gene expression levels of a set of known target genes. We treat the protein concentration as a latent function with a Gaussian process prior, and include the sensitivities, mRNA decay rates and baseline expression levels as hyperparameters. We apply this procedure to a human leukemia dataset, focusing on the tumour repressor p53 and obtaining results in good accordance with recent biological studies.

  • The Challenges of Systems Biology

    This chapter contains sections titled: 1 Genomics, Gene Expression, and Next- Generation Sequencing, 2 Metabolic Network Reconstruction, 3 Computational Models of Gene Translation, 4 Reverse Engineering of Cellular Networks, 5 Outlook, Note, References

  • The Use of Emerging Patterns in the Analysis of Gene Expression Profiles for the Diagnosis and Understanding of Diseases

    This chapter contains sections titled: Introduction Prediction by Collective Likelihood Based on Emerging Patterns Selection of Relevant Genes Diagnosis of Disease State or Subtype Derivation of Treatment Plan Understanding of Molecular Circuit Closing Remarks This chapter contains sections titled: References

  • Clustering Functionally Similar Genes from Microarray Data

    This chapter deals with the application of different rough-fuzzy clustering algorithms for clustering functionally similar genes from microarray gene expression data sets. The effectiveness of the algorithms, along with a comparison with other related gene clustering algorithms, is demonstrated on a set of microarray gene expression data sets using some standard validity indices. The chapter first reports a brief overview of different gene clustering algorithms. It then describes several quantitative and qualitative performance measures such as Silhouette index, Eisen and cluster profile plots, Z score, gene-ontology-based analysis to evaluate the quality of gene clusters. The chapter presents a brief description of different microarray gene expression data sets such as fifteen yeast data, yeast sporulation, Auble data, Cho et al. data, and reduced cell cycle data. It also presents implementation details, experimental results, and a comparison among different algorithms. fuzzy set theory; pattern clustering; performance evaluation; rough set theory

  • Symbols and Abbreviations

    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.

  • Microarrays and Gene Expression

    This chapter contains sections titled: Introduction to Microarray Data, Probabilistic Modeling of Array Data, Clustering, Gene Regulation

  • 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



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