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HiCOMB 2018 - 17th IEEE International Workshop on High Performance Computational Biology

Date2018-05-21

Deadline2018-01-30

VenueVancouver, BC, Canada Canada

Keywords

Websitehttp://www.hicomb.org

Topics/Call fo Papers

The size and complexity of genome- and proteome-scale data sets in bioinformatics continues to grow at a furious pace, and the analysis of these complex, noisy, data sets demands efficient algorithms and high performance computer architectures. Hence high-performance computing has become an integral part of research and development in bioinformatics, computational biology, and medical and health informatics. The goal of this workshop is to provide a forum for discussion of latest research in developing high-performance computing solutions to data- and compute-intensive problems arising from all areas of computational life sciences. We are especially interested in parallel and distributed algorithms, memory-efficient algorithms, large scale data mining techniques including approaches for big data and cloud computing, algorithms on multicores, many-cores and GPUs, and design of high-performance software and hardware for biological applications.
The workshop will feature contributed papers as well as invited talks from reputed researchers in the field.
Topics of interest include but are not limited to:
Bioinformatics data analytics
Biological network analysis
Cloud-enabled solutions for computational biology
Computational genomics and metagenomics
Computational proteomics and metaproteomics
DNA assembly, clustering, and mapping
Energy-aware high performance biological applications
Gene identification and annotation
High performance algorithms for computational systems biology
High throughput, high dimensional data analysis: flow cytometry and related proteomic data
Parallel algorithms for biological sequence analysis
Molecular evolution and phylogenetic reconstruction algorithms
Protein structure prediction and modeling
Parallel algorithms in chemical genetics and chemical informatics
Transcriptome analysis with RNASeq

Last modified: 2017-12-22 23:09:36