Genomics

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Genomics is a discipline in genetics concerning the study of the genomes of organisms. (Wikipedia.org)






Conferences related to Genomics

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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

  • 2007 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)

  • 2006 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)

  • 2005 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)


2012 5th International Conference on Biomedical Engineering and Informatics (BMEI)

BMEI is a premier international forum for scientists and researchers to present the state of the art of biomedical engineering and informatics. Specific topics include Biomedical imaging and visualization; Biomedical signal processing and analysis; etc.

  • 2011 4th International Conference on Biomedical Engineering and Informatics (BMEI)

    CISP 11-BMEI 11 is a premier international forum for scientists and researchers to present the state of the art of biomedical engineering and informatics. Specific topics include Biomedical imaging and visualization; Biomedical signal processing and analysis; Biomedical instrumentation, devices, sensors, artificial organs, and nano technologies; Rehabilitation engineering; bioinformatics and medical informatics, etc.

  • 2010 3rd International Conference on Biomedical Engineering and Informatics (BMEI)

    BMEI 10 is a premier international forum for scientists and researchers to present the state of the art of biomedical engineering and biomedical informatics. It is co-located with the 3rd International Congress on Image and Signal Processing (CISP 2010) to promote interactions biomedical research and signal processing.


2012 IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE)

The annual IEEE International Conference on Bioinformatics and Bioengineering covers complementary disciplines that hold great promise for the advancement of research and development in complex medical and biological systems, agriculture, environment, public health, drug design, and so on.

  • 2011 IEEE 11th International Conference on Bioinformatics & Bioengineering (BIBE)

    The annual IEEE International Conference on Bioinformatics and Bioengineering aims at building synergy between Bioinformatics and Bioengineering, two complementary disciplines that hold great promise for the advancement of research and development in complex medical and biological systems, agriculture, environment, public health, drug design.

  • 2010 International Conference on BioInformatics and BioEngineering (BIBE)

  • 2009 9th IEEE International Conference on BioInformatics and BioEngineering - BIBE

    The annual IEEE International Conference on Bioinformatics and Bioengineering aims at building synergy between Bioinformatics and Bioengineering, two complementary disciplines that hold great promise for the advancement of research and development in complex medical and biological systems, agriculture, environment, public health, drug design. Research and development in these two areas are impacting the science and technology in fields such as medicine, food production, forensics, etc.

  • 2008 8th IEEE International Conference on BioInformatics and BioEngineering - BIBE

    The annual IEEE International Conference on Bioinformatics and Bioengineering aims at building synergy between Bioinformatics and Bioengineering, two complementary disciplines that hold great promise for the advancement of research and development in complex medical and biological systems, agriculture, environment, public health, drug design.

  • 2007 7th IEEE International Conference on BioInformatics and BioEngineering - BIBE

    Bioinformatics and Bioengineering are complementary disciplines that hold great promise for the advancement of research and development in complex medical and biological systems, agriculture, environment, public health, drug design, and so on. Research and development in these two areas are impacting the science and technology of fields such as medicine, food production, forensics, etc. by advancing fundamental concepts in molecular biology and in medicine ,by helping us understand living organisms.


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.


2012 Portland International Conference on Management of Engineering & Technology (PICMET)

PICMET's focus is on bringing together the experts on technology management to address the issues involved in managing current and emerging technologies.


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Periodicals related to Genomics

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Information Technology in Biomedicine, IEEE Transactions on

Telemedicine, teleradiology, telepathology, telemonitoring, telediagnostics, 3D animations in health care, health information networks, clinical information systems, virtual reality applications in medicine, broadband technologies, and global information infrastructure design for health care.


Knowledge and Data Engineering, IEEE Transactions on

Artificial intelligence techniques, including speech, voice, graphics, images, and documents; knowledge and data engineering tools and techniques; parallel and distributed processing; real-time distributed processing; system architectures, integration, and modeling; database design, modeling, and management; query design, and implementation languages; distributed database control; statistical databases; algorithms for data and knowledge management; performance evaluation of algorithms and systems; data communications aspects; system ...




Xplore Articles related to Genomics

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Uncover protein complexes in E.coli network

Wei Liu; Aiping Wu 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2015

Recent advances in proteomic technologies have enabled high-throughput binary data on protein-protein interactions of E. coli to be released into public domain, and many protein complexes have been identified by experimental methods. Although it has a long study history, a large-scale analysis of protein complex in binary PPI network of E. coli is still absent. We used a novel link ...


Microwave assisted trypsin digestion with cavity type resonator reactor as an innovative proteomics technology

Fujiko Aoki; Kenshi Haraguchi; Arata Shiraishi; Takeo Yoshimura; Shokichi Ohuchi 2016 Progress in Electromagnetic Research Symposium (PIERS), 2016

Sumamry form only given. The word "omics" indicates the study of a total information in biological cell, such as the genome, which is all DNA in a living cell, or the proteome which is all the proteins. Omics technologies of genomics and proteomics, such as tailor-made treatment, it has been positioned in the center of the future of medical technology. ...


A* fast and scalable high-throughput sequencing data error correction via oligomers

Franco Milicchio; Iain E. Buchan; Mattia C. F. Prosperi 2016 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2016

Next-generation sequencing (NGS) technologies have superseded traditional Sanger sequencing approach in many experimental settings, given their tremendous yield and affordable cost. Nowadays it is possible to sequence any microbial organism or meta-genomic sample within hours, and to obtain a whole human genome in weeks. Nonetheless, NGS technologies are error-prone. Correcting errors is a challenge due to multiple factors, including the ...


Patho-finder ??? A fast and accurate program for pathogen identification through RNA-seq

Chin-Ting Wu; Tzu-Hung Hsiao; Yu-Chiao Chiu; Yu-Ching Hsu; Eric Y. Chuang; Yidong Chen 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2015

Technology of next generation sequencing to detect pathogens of sample can impact human health by revealing pathogens which cause disease. Several workflow has developed in purposed to detect pathogens in next generation sequencing data. However, the requirement of computation power of these workflows limited the application. The time consuming problem make the workflow difficult to detect datasets with large sample ...


Mining knowledge for the methylation status of CpG islands using alternating decision trees

Matthew B. Carson; Robert Langlois; Hui Lu 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2008

CpG island (CpGI) methylation is an epigenetic modification that occurs in eukaryotes and is based on the addition of a methyl group to the number 5 carbon of the pyrimidine ring of cytosine. When methylation of a CpGI occurs, the associated gene (if any) is not expressed [1]. Aberrant methylation is thought to be a causative agent in disease [2] ...


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Educational Resources on Genomics

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eLearning

Uncover protein complexes in E.coli network

Wei Liu; Aiping Wu 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2015

Recent advances in proteomic technologies have enabled high-throughput binary data on protein-protein interactions of E. coli to be released into public domain, and many protein complexes have been identified by experimental methods. Although it has a long study history, a large-scale analysis of protein complex in binary PPI network of E. coli is still absent. We used a novel link ...


Microwave assisted trypsin digestion with cavity type resonator reactor as an innovative proteomics technology

Fujiko Aoki; Kenshi Haraguchi; Arata Shiraishi; Takeo Yoshimura; Shokichi Ohuchi 2016 Progress in Electromagnetic Research Symposium (PIERS), 2016

Sumamry form only given. The word "omics" indicates the study of a total information in biological cell, such as the genome, which is all DNA in a living cell, or the proteome which is all the proteins. Omics technologies of genomics and proteomics, such as tailor-made treatment, it has been positioned in the center of the future of medical technology. ...


A* fast and scalable high-throughput sequencing data error correction via oligomers

Franco Milicchio; Iain E. Buchan; Mattia C. F. Prosperi 2016 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2016

Next-generation sequencing (NGS) technologies have superseded traditional Sanger sequencing approach in many experimental settings, given their tremendous yield and affordable cost. Nowadays it is possible to sequence any microbial organism or meta-genomic sample within hours, and to obtain a whole human genome in weeks. Nonetheless, NGS technologies are error-prone. Correcting errors is a challenge due to multiple factors, including the ...


Patho-finder ??? A fast and accurate program for pathogen identification through RNA-seq

Chin-Ting Wu; Tzu-Hung Hsiao; Yu-Chiao Chiu; Yu-Ching Hsu; Eric Y. Chuang; Yidong Chen 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2015

Technology of next generation sequencing to detect pathogens of sample can impact human health by revealing pathogens which cause disease. Several workflow has developed in purposed to detect pathogens in next generation sequencing data. However, the requirement of computation power of these workflows limited the application. The time consuming problem make the workflow difficult to detect datasets with large sample ...


Mining knowledge for the methylation status of CpG islands using alternating decision trees

Matthew B. Carson; Robert Langlois; Hui Lu 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2008

CpG island (CpGI) methylation is an epigenetic modification that occurs in eukaryotes and is based on the addition of a methyl group to the number 5 carbon of the pyrimidine ring of cytosine. When methylation of a CpGI occurs, the associated gene (if any) is not expressed [1]. Aberrant methylation is thought to be a causative agent in disease [2] ...


More eLearning Resources

IEEE-USA E-Books

  • Introduction

    Sparse modeling is a rapidly developing area at the intersection of statistical learning and signal processing, motivated by the age-old statistical problem of selecting a small number of predictive variables in high-dimensional datasets. This collection describes key approaches in sparse modeling, focusing on its applications in fields including neuroscience, computational biology, and computer vision. Sparse modeling methods can improve the interpretability of predictive models and aid efficient recovery of high-dimensional unobserved signals from a limited number of measurements. Yet despite significant advances in the field, a number of open issues remain when sparse modeling meets real-life applications. The book discusses a range of practical applications and state- of-the-art approaches for tackling the challenges presented by these applications. Topics considered include the choice of method in genomics applications; analysis of protein mass-spectrometry data; the sta ility of sparse models in brain imaging applications; sequential testing approaches; algorithmic aspects of sparse recovery; and learning sparse latent models. **Contributors**A. Vania Apkarian, Marwan Baliki, Melissa K. Carroll, Guillermo A. Cecchi, Volkan Cevher, Xi Chen, Nathan W. Churchill, R??mi Emonet, Rahul Garg, Zoubin Ghahramani, Lars Kai Hansen, Matthias Hein, Katherine Heller, Sina Jafarpour, Seyoung Kim, Mladen Kolar, Anastasios Kyrillidis, Aurelie Lozano, Matthew L. Malloy, Pablo Meyer, Shakir Mohamed, Alexandru Niculescu-Mizil, Robert D. Nowak, Jean-Marc Odobez, Peter M. Rasmussen, Irina Rish, Saharon Rosset, Martin Slawski, Stephen C. Strother, Jagannadan Varadarajan, Eric P. Xing

  • Introduction

    Computational molecular biology, or bioinformatics, draws on the disciplines of biology, mathematics, statistics, physics, chemistry, computer science, and engineering. It provides the computational support for functional genomics, which links the behavior of cells, organisms, and populations to the information encoded in the genomes, as well as for structural genomics. At the heart of all large-scale and high-throughput biotechnologies, it has a growing impact on health and medicine.This survey of computational molecular biology covers traditional topics such as protein structure modeling and sequence alignment, and more recent ones such as expression data analysis and comparative genomics. It combines algorithmic, statistical, database, and AI- based methods for studying biological problems. The book also contains an introductory chapter, as well as one on general statistical modeling and computational techniques in molecular biology. Each chapter presents a self- contained review of a specific subject.Not for sale in China, including Hong Kong

  • Contributors

    Computational molecular biology, or bioinformatics, draws on the disciplines of biology, mathematics, statistics, physics, chemistry, computer science, and engineering. It provides the computational support for functional genomics, which links the behavior of cells, organisms, and populations to the information encoded in the genomes, as well as for structural genomics. At the heart of all large-scale and high-throughput biotechnologies, it has a growing impact on health and medicine.This survey of computational molecular biology covers traditional topics such as protein structure modeling and sequence alignment, and more recent ones such as expression data analysis and comparative genomics. It combines algorithmic, statistical, database, and AI- based methods for studying biological problems. The book also contains an introductory chapter, as well as one on general statistical modeling and computational techniques in molecular biology. Each chapter presents a self- contained review of a specific subject.Not for sale in China, including Hong Kong

  • Data Mining and Pattern Discovery

    Computational molecular biology, or bioinformatics, draws on the disciplines of biology, mathematics, statistics, physics, chemistry, computer science, and engineering. It provides the computational support for functional genomics, which links the behavior of cells, organisms, and populations to the information encoded in the genomes, as well as for structural genomics. At the heart of all large-scale and high-throughput biotechnologies, it has a growing impact on health and medicine.This survey of computational molecular biology covers traditional topics such as protein structure modeling and sequence alignment, and more recent ones such as expression data analysis and comparative genomics. It combines algorithmic, statistical, database, and AI- based methods for studying biological problems. The book also contains an introductory chapter, as well as one on general statistical modeling and computational techniques in molecular biology. Each chapter presents a self- contained review of a specific subject.Not for sale in China, including Hong Kong

  • The Data Citizen, the Quantified Self, and Personal Genomics

    This chapter contains sections titled: Introduction: We Are All "Data Citizens", Quantified Selves and Personal Genomics: Bioscience, Biosensors, Contexts, and Practices, Concluding Thoughts, Acknowledgments, Notes, References

  • Computational Analysis of Interactions between Tumor and Tumor Suppressor Proteins

    This chapter contains sections titled: Introduction Methodology: Resonant Recognition Model Results and Discussions Conclusion 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

  • 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 Challenges Facing Genomic Informatics

    Computational molecular biology, or bioinformatics, draws on the disciplines of biology, mathematics, statistics, physics, chemistry, computer science, and engineering. It provides the computational support for functional genomics, which links the behavior of cells, organisms, and populations to the information encoded in the genomes, as well as for structural genomics. At the heart of all large-scale and high-throughput biotechnologies, it has a growing impact on health and medicine.This survey of computational molecular biology covers traditional topics such as protein structure modeling and sequence alignment, and more recent ones such as expression data analysis and comparative genomics. It combines algorithmic, statistical, database, and AI- based methods for studying biological problems. The book also contains an introductory chapter, as well as one on general statistical modeling and computational techniques in molecular biology. Each chapter presents a self- contained review of a specific subject.Not for sale in China, including Hong Kong

  • Graph Theoretic Models in Chemistry and Molecular Biology

    The field of chemical graph theory utilizes simple graphs as models of molecules. These models are called molecular graphs, and quantifiers of molecular graphs are known as molecular descriptors or topological indices. Today's chemists use molecular descriptors to develop algorithms for computer aided drug designs, and computer based searching algorithms of chemical databases and the field is now more commonly known as combinatorial or computational chemistry. With the completion of the human genome project, related fields are emerging such as chemical genomics and pharmacogenomics. Recent advances in molecular biology are driving new methodologies and reshaping existing techniques, which in turn produce novel approaches to nucleic acid modeling and protein structure prediction. The origins of chemical graph theory are revisited and new directions in combinatorial chemistry with a special emphasis on biochemistry are explored. Of particular importance is the extension of the set of molecular descriptors to include graphical invariants. We also describe the use of artificial neural networks (ANNs) in predicting biological functional relationships based on molecular descriptor values. Specifically, a brief discussion of the fundamentals of ANNs together with an example of a graph theoretic model of RNA to illustrate the potential for ANN coupled with graphical invariants to predict function and structure of biomolecules is included.



Standards related to Genomics

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No standards are currently tagged "Genomics"


Jobs related to Genomics

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