9,238 resources related to Genomics
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- Most published Xplore authors for Genomics
The conference program will consist of plenary lectures, symposia, workshops andinvitedsessions of the latest significant findings and developments in all the major fields ofbiomedical engineering.Submitted papers will be peer reviewed. Accepted high quality paperswill be presented in oral and postersessions, will appear in the Conference Proceedings and willbe indexed in PubMed/MEDLINE & IEEE Xplore
The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging.ISBI 2019 will be the 16th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2019 meeting will continue this tradition of fostering cross fertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.
2019 IEEE International Conference on Systems, Man, and Cybernetics (SMC2019) will be held in the south of Europe in Bari, one of the most beautiful and historical cities in Italy. The Bari region’s nickname is “Little California” for its nice weather and Bari's cuisine is one of Italian most traditional , based of local seafood and olive oil. SMC2019 is the flagship conference of the IEEE Systems, Man, and Cybernetics Society. It provides an international forum for researchers and practitioners to report up-to-the-minute innovations and developments, summarize stateof-the-art, and exchange ideas and advances in all aspects of systems science and engineering, human machine systems and cybernetics. Advances have importance in the creation of intelligent environments involving technologies interacting with humans to provide an enriching experience, and thereby improve quality of life.
Provides a full program of technical and professional activities spanning hot topics in voice, data, image and multimedia communications and networking.
Industrial Informatics, Computational Intelligence, Control and Systems, Cyber-physicalSystems, Energy and Environment, Mechatronics, Power Electronics, Signal and InformationProcessing, Network and Communication Technologies
Speech analysis, synthesis, coding speech recognition, speaker recognition, language modeling, speech production and perception, speech enhancement. In audio, transducers, room acoustics, active sound control, human audition, analysis/synthesis/coding of music, and consumer audio. (8) (IEEE Guide for Authors) The scope for the proposed transactions includes SPEECH PROCESSING - Transmission and storage of Speech signals; speech coding; speech enhancement and noise reduction; ...
Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.
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; ...
Computer, the flagship publication of the IEEE Computer Society, publishes peer-reviewed technical content that covers all aspects of computer science, computer engineering, technology, and applications. Computer is a resource that practitioners, researchers, and managers can rely on to provide timely information about current research developments, trends, best practices, and changes in the profession.
IEEE Computer Graphics and Applications (CG&A) bridges the theory and practice of computer graphics. From specific algorithms to full system implementations, CG&A offers a strong combination of peer-reviewed feature articles and refereed departments, including news and product announcements. Special Applications sidebars relate research stories to commercial development. Cover stories focus on creative applications of the technology by an artist or ...
2016 International Workshop on Big Data and Information Security (IWBIS), 2016
With the rapid development of theory and practice in Genomics, research on Public Health Genomics, as a new field is beginning to contribute to people's life. A large volume of genomics data is available but not yet readily used in clinical services. A gap exists between genomics research and public healthcare genomics applications. We believe that machine intelligence can play ...
2017 Data Compression Conference (DCC), 2017
Genomics is a Big Data science, the rate of increase in DNA sequencing is significantly exceeding the rate of increase in storage capacity, study shows the genomics data generation will exceed Twitter, YouTube, and astrophysics data combined by the year 2025. Storage and data management have become one of the most challenging bottlenecks in genomics and life sciences research. Data ...
2018 IEEE International Conference on Cluster Computing (CLUSTER), 2018
SVE (Scalable Vector Extension) is Arm's new vector instruction extension targeting high performance workloads. SVE offers many opportunities to optimise compute intensive workloads but, with the availability of SVE-enabled hardware still years away, we have to rely on simulation techniques in order to evaluate our implementations. Working with simulators can be tricky and it comes with many limitations but, used ...
2014 IEEE International Symposium on Ethics in Science, Technology and Engineering, 2014
The issues of privacy and disclosure are two sides of a weighty coin. Computational biologists and other scientists involved in genomic research need to be constantly cognizant of the push and pull of these two important concepts. Clinical genomics research in particular raises a number of particularly poignant concerns as society struggles between invasions of privacy such as recent efforts ...
2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE), 2017
In this paper we investigate the use of Unix named pipes and an in-memory datagrid to reduce the I/O requirements of conventional and exploratory genomics processing pipelines. Apache Spark provides an in-memory framework for distributed computational genomics that has realized significant improvements over conventional pipelines in speed and flexibility. Even in the Spark framework, however, pipeline components create I/O bottlenecks ...
Microfluidic devices for precision biological measurement: Stephen Quake
EMBC 2011-Keynote Lectures and Panel Discussion-PT I-Subra Suresh
A Conversation About Mind/Brain Research and AI Development: IEEE TechEthics Interview
Mind/Brain Research and AI Development: How Do They Inform Each Other? - IEEE TechEthics Panel
With the rapid development of theory and practice in Genomics, research on Public Health Genomics, as a new field is beginning to contribute to people's life. A large volume of genomics data is available but not yet readily used in clinical services. A gap exists between genomics research and public healthcare genomics applications. We believe that machine intelligence can play an important role in transferring genomics knowledge to practical use. As a vision of our research, in this paper we present the usefulness of applying machine intelligence to public health genomics.
Genomics is a Big Data science, the rate of increase in DNA sequencing is significantly exceeding the rate of increase in storage capacity, study shows the genomics data generation will exceed Twitter, YouTube, and astrophysics data combined by the year 2025. Storage and data management have become one of the most challenging bottlenecks in genomics and life sciences research. Data compression is an important technique to improve the efficiency of genomics data analysis and storage, and is widely deployed in the IT infrastructure in the life science institutes. In this paper we analyze the data compression characterization in the genomics workflows, and evaluate the performance & cost for different compression algorithms in the genomics analysis tool, we present a new hardware acceleration method that efficiently compresses DNA sequences with the reduced computation time and CPU utilization.
SVE (Scalable Vector Extension) is Arm's new vector instruction extension targeting high performance workloads. SVE offers many opportunities to optimise compute intensive workloads but, with the availability of SVE-enabled hardware still years away, we have to rely on simulation techniques in order to evaluate our implementations. Working with simulators can be tricky and it comes with many limitations but, used properly, a simulator like Gem5 is a valuable tool that can provide an opportunity to explore the possibilities opened by this new extension. As a use case, we focus our attention on the field of genomics, where the recent advent of high-throughput sequencing machines producing big amounts of genomic data has boosted the interest in efficient approximate string matching and alignment techniques. Genomics algorithms are the key computational building blocks for the downstream data analysis on resequencing projects where hundreds of GBytes of sequenced data are analysed against a reference genome in order to filter sequencing errors and detect potential genomic variation events. The computational requirements and sheer size of the input data used by these applications make them a challenging problem. Fortunately, they also exhibit a high degree of data parallelism, making them good candidates for vectorisation techniques. In this work we explore the unique opportunities that SVE provides in order to exploit the parallelism present in genomics algorithms. We discuss preliminary results, our simulation strategy, some of the obstacles and limitations we faced, and how to work around them in order to obtain meaningful results.
The issues of privacy and disclosure are two sides of a weighty coin. Computational biologists and other scientists involved in genomic research need to be constantly cognizant of the push and pull of these two important concepts. Clinical genomics research in particular raises a number of particularly poignant concerns as society struggles between invasions of privacy such as recent efforts by the FBI and the NSA, and our own (surprisingly) personal disclosures on social media sites or via apathetic acquiescence to large data collection efforts. With regard to privacy there are numerous computational efforts that have heretofore offered to provide both the robustness of protection and the ease of use to be effective in manipulating the terabytes of data before the genomics researcher. Unfortunately algorithms alone have thus far failed to provide either the necessary strength to foil those intent on obtaining information or the promised agility to manipulate the vast datasets. While technical solutions advance, they cannot stand on their own and this paper proposes and outlines a licensing scheme, similar to those used by professional organizations, that not only enforce a code of conduct and punish those who fail to live up to that code, but also mandate required continuing education to limit the possibility that the code will be violated inadvertently. It is the use of the social and the technological advances together that will likely create not only an environment that fosters research and innovation, but also one that is responsive to privacy needs and norms.
In this paper we investigate the use of Unix named pipes and an in-memory datagrid to reduce the I/O requirements of conventional and exploratory genomics processing pipelines. Apache Spark provides an in-memory framework for distributed computational genomics that has realized significant improvements over conventional pipelines in speed and flexibility. Even in the Spark framework, however, pipeline components create I/O bottlenecks by reading and writing intermediate files that are later discarded. Apache Ignite provides a framework for persisting a Spark dataset in memory between modular pipeline applications, and Unix named pipes have long provided a mechanism by which data can be transferred in-memory. We compared the runtime performance of a standard genomics pipeline that transmits Spark data using named pipes and/or Ignite's in-memory datagrid. Our results demonstrate that Ignite can improve the runtime performance of in-memory RDD actions and that keeping pipeline components in memory with Ignite and named pipes eliminates a major I/O bottleneck.
Traditional retrieval models assume that the relevance of a document is independent of the relevance of other documents. However, this assumption may result in high redundancy and low diversity in a ranked list. In order to provide comprehensive and diverse answers to fulfill biologists' information need, we propose a relevance-novelty combined model, named RelNov model, based on the framework of an undirected graphical model. Experiments conducted on the TREC 2006 and 2007 Genomics collections show that the proposed approach is effective in promoting both diversity and relevance of retrieval ranked lists.
In the epic endeavor to sequence the human genome, it was as though the size of the equipment and amount of effort required were inversely proportional to the microscopic materials being parsed (Figure 1). "Imagine thousands of people, many laboratories⋯hundreds, hundreds, hundreds of these machines just to generate that first human genome sequence over a six to eight year period," says Dr. Eric Green, director of the National Human Genome Research Institute (NHGRI) at the National Institutes of Health (NIH) (Figure 2).
Combination of multiple evidences has been shown to be effective in genomics literature retrieval. Citation information is an intuitive evidence for facilitating literature retrieval. Previous research on citation analysis has demonstrated that useful linkage information can be extracted from the citation graph. However, the question of how the combination of citation evidence and content evidence should be done to maximize retrieval accuracy still remains largely unanswered. In this paper, we propose BioCLink, a new probabilistic approach that integrates citation evidence into content-based weighting function for improving genomics literature retrieval performance. Based on findings of our previous study, a strategy for modeling citation evidence is proposed. BioCLink provides the combination of content and citation evidences with a theoretical support. Moreover, exhaustiveparameter tuning can be avoided using BioCLink. Extensive experiments on TREC 2006 and 2007 Genomics collections demonstrate the advantages and effectiveness of our proposed methods.
Many genome-scale data are available in soybean including genomic sequence, transcriptomics (microarray, RNA-seq), proteomics and metabolomics datasets, together with growing knowledge of soybean in gene, microRNAs, pathways, and phenotypes. This represents rich and resourceful information which can provide valuable insights, if mined in an innovative and integrative manner and thus, the need for informatics resources to achieve that. Towards this we have developed Soybean Knowledge Base (SoyKB), a comprehensive all-inclusive web resource for soybean translational genomics and breeding. SoyKB handles the management and integration of soybean genomics and multi-omics data along with gene function annotations, biological pathway and trait information. It has many useful tools including Affymetrix probelD search, gene family search, multiple gene/metabolite analysis, motif analysis tool, protein 3D structure viewer and download/upload capacity for experimental data and annotations. It has a user-friendly web interface together with genome browser and pathway viewer, which display data in an intuitive manner to the soybean researchers, breeders and consumers. SoyKB has new innovative tools for soybean breeding including a graphical chromosome visualizer targeted towards ease of navigation for breeders. It integrates QTLs, traits, germplasm information along with genomic variation data such as single nucleotide polymorphisms (SNPs) and genome-wide association studies (GWAS) data from multiple genotypes, cultivars and G. soja. QTLs for multiple traits can be queried and visualized in the chromosome visualizer simultaneously and overlaid on top of the genes and other molecular markers as well as multi-omics experimental data for meaningful inferences.
Genomics and molecular biology at the inspiration and motivation researchers worldwide in biology and biotechnology. Both areas controlled by many data, and grouped and analysed bioinformatics the past. The application of bioinformatics is fast and efficient a clear vision of all this analysis and data to reduce costly laboratory equipment, chemicals and the most valuable time. Most data, including genome sequence results in large numbers, and that is why the administration manual curation of data very difficult. The purpose of the study on the selection of the highest awareness of cancer genomics and next-generation genome sequencing bioinformatics to create different viruses. A next-generation sequencing and high-throughput sequencing could replace the old method of sequencing using the latest technology. This technology is very efficient, faster and cheaper than the traditional way. Normal use, briefly discussed below. The role of bioinformatics is increasingly and managing large amounts of data in the world of medical research, biotechnology and clinical analysis. But we still need to understand the challenges and limitations of bioinformatics and reliable.
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