Genomics

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






Conferences related to Genomics

Back to Top

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

  • 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.


More Conferences

Periodicals related to Genomics

Back to Top

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

Back to Top

Micro-repetitive Structure of Genomic Sequences and the Identification of Ancient Repeat Elements

Abanish Singh; Cedric Feschotte; Nikola Stojanovic 2007 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2007), 2007

For many years the attempts to identify functional elements in genomic sequences through motif over- representation have been problematic, as every procedure to isolate such motifs resulted in a very large number of candidates with highly significant p-values. In this paper we postulate that most of these elements originate in ancient transpositional activity, with copies becoming so broken over time ...


Predicting Markov Chain Order in Genomic Sequences

Lenwood S. Heath; Amrita Pati 2007 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2007), 2007

Genomic sequences display characteristic features at various scales ranging from oligonucleotide frequencies to large organizational units such as genes. The generation of such a sequence, defined as a string over the alphabet SigmaDNA={A C, T, G}, can be approximated by a formal machine, a Markov chain having strings as states, whose parameters lend unique characteristics to the sequence. We present ...


Analysis of genome signature strength of SARS coronavirus using Self-Organizing Map neural network

Francis Thamburaj; Gopinath Ganapathy 2010 International Conference on Communication and Computational Intelligence (INCOCCI), 2010

The nucleotide usage patterns vary not only from organism to organism, but also between genes in the same genome. Each genome has its own characteristics. This unique identity, called genome signature, of a genome is multidimensional. One of the ways to probe into this area is to analyze the nucleotide sequence composition of the genome. In this paper, the nucleotide ...


Combining Simulation and Machine Learning to Recognize Function in 4D

Russ Biagio Altman 2007 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2007), 2007

This paper is a talk by Russ Biagio Altman. It discusses structure-based protein function annotation using machine learning, physics-based simulation of structure, and how they can be profitably combined to improve our understanding of molecular structure and function.


Multi-agent System for Translation Initiation Site Prediction

Jia Zeng; Reda Alhajj 2007 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2007), 2007

Accurate translation initiation site (TIS) prediction is very important for genomic analysis. It is a com- mon understanding that analyzing the large amount of genomic data by pure biological methods is impracti- cal if not impossible. Therefore many approaches have been proposed which apply some machine learning tech- nique to analyze a particular aspect of the data. We believe, however, ...


More Xplore Articles

Educational Resources on Genomics

Back to Top

eLearning

Micro-repetitive Structure of Genomic Sequences and the Identification of Ancient Repeat Elements

Abanish Singh; Cedric Feschotte; Nikola Stojanovic 2007 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2007), 2007

For many years the attempts to identify functional elements in genomic sequences through motif over- representation have been problematic, as every procedure to isolate such motifs resulted in a very large number of candidates with highly significant p-values. In this paper we postulate that most of these elements originate in ancient transpositional activity, with copies becoming so broken over time ...


Predicting Markov Chain Order in Genomic Sequences

Lenwood S. Heath; Amrita Pati 2007 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2007), 2007

Genomic sequences display characteristic features at various scales ranging from oligonucleotide frequencies to large organizational units such as genes. The generation of such a sequence, defined as a string over the alphabet SigmaDNA={A C, T, G}, can be approximated by a formal machine, a Markov chain having strings as states, whose parameters lend unique characteristics to the sequence. We present ...


Analysis of genome signature strength of SARS coronavirus using Self-Organizing Map neural network

Francis Thamburaj; Gopinath Ganapathy 2010 International Conference on Communication and Computational Intelligence (INCOCCI), 2010

The nucleotide usage patterns vary not only from organism to organism, but also between genes in the same genome. Each genome has its own characteristics. This unique identity, called genome signature, of a genome is multidimensional. One of the ways to probe into this area is to analyze the nucleotide sequence composition of the genome. In this paper, the nucleotide ...


Combining Simulation and Machine Learning to Recognize Function in 4D

Russ Biagio Altman 2007 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2007), 2007

This paper is a talk by Russ Biagio Altman. It discusses structure-based protein function annotation using machine learning, physics-based simulation of structure, and how they can be profitably combined to improve our understanding of molecular structure and function.


Multi-agent System for Translation Initiation Site Prediction

Jia Zeng; Reda Alhajj 2007 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2007), 2007

Accurate translation initiation site (TIS) prediction is very important for genomic analysis. It is a com- mon understanding that analyzing the large amount of genomic data by pure biological methods is impracti- cal if not impossible. Therefore many approaches have been proposed which apply some machine learning tech- nique to analyze a particular aspect of the data. We believe, however, ...


More eLearning Resources

IEEE-USA E-Books

  • Interactomics

    This chapter contains sections titled: Interactomics and Omics Sciences Genomics and Proteomics Representation and Management of Protein Interaction Data Analysis of Protein Interaction Networks Visualization of Protein Interaction Networks Models for Biological Networks Flow of Information in Interactomics Applications of Interactomics in Biology and Medicine Summary

  • Contributors

    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

  • Molecular Bioengineering and Nanobioscience: Data Analysis and Processing Methods

    This chapter contains sections titled: Introduction Data Analysis and Processing Methods for Genomics in the Postgenomic Era From Genomics to Proteomics Protein Structure Determination Conclusions

  • Index

    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

  • 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

  • Index

    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

  • Frontmatter

    The prelims comprise: Half Title Wiley Series Page Title Copyright Dedication Contents Preface Contributors

  • Index

    No abstract.

  • 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

  • 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



Standards related to Genomics

Back to Top

No standards are currently tagged "Genomics"


Jobs related to Genomics

Back to Top