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ParBio 2020 : 9th International Workshop on Parallel and Cloud-based Bioinformatics and Biomedicine (ParBio)


When Sep 21, 2020 - Sep 21, 2020
Where Online/Virtual (Atlanta, GA USA)
Submission Deadline Jul 6, 2020
Notification Due Jul 20, 2020
Final Version Due Jul 29, 2020
Categories    bioinformatics   cloud computing   HPC   parallel

Call For Papers


Due to the availability of high-throughput platforms (e.g. next generation sequencing, microarray, and mass spectrometry) and clinical diagnostic tools (e.g. medical imaging), a recent trend in Bioinformatics and Biomedicine is the increasing production of experimental and clinical data. Considering the complex analysis pipelines often used in biomedical research, a key bottleneck involves the storage, integration, and analysis of experimental data, as well as the correlation and integration of analyses with data from publicly available datasets. While parallel computing and Grid computing may offer the computational power and the storage to face this overwhelming availability of data, Cloud Computing is a key technology to hide the complexity of computing infrastructures, while reducing the cost of the data analyses, ultimately serving to change the overall model of biomedical research and health care delivery.

Traditionally, grid infrastructures have offered the data storage needed to store the huge experimental and biomedical data, while parallel computing has been used for basic pre-processing (e.g. parallel BLAST, mpiBLAST) and for more advanced analysis (e.g. parallel data mining). Recently, novel parallel architectures (e.g. CELL processors, GPUs, FPGAs, hybrid CPU/FPGA) coupled with emerging programming models may overcome the limits posed by conventional computers to the mining and exploration of large amounts of data. These technologies, however, require great investments by biomedical and clinical institutions, and are based on a traditional model where users often need to be aware and face different management problems, such as hardware and software management, data storage, software ownership, with potentially insurmountable start-up costs (e.g. different professional-level applications in the biomedical domain have high start-up costs that often prevent many small laboratories from using them).

Cloud Computing technology, that is able to offer scalable costs with increased accessibility, availability, and ease of application use, while enhancing the potential for collaboration among scientists, is already changing the business model in different sectors and has been adopted in the bioinformatics and biomedical domains. However, many problems remain to be solved, such as availability and safety of the data, privacy-related issues, availability of software platforms for rapid deployment, and the execution and billing of biomedical applications.

The Cloud Computing technology, that is able to offer scalable costs and increased reachability, availability and easiness of application use, and also the possibility to enable collaboration among scientists, is already changing the business model in different domains and now it starts to be used also in the bioinformatics (see for instance the recent JCVI Cloud Bio-Linux initiative) and biomedical domains. However, many problems remain to be solved, such as availability and safety of the data, privacy-related issues, availability of software platforms for rapid deployment, execution and billing of biomedical applications.

The goal of ParBio 2020 is to bring together scientists in the fields of high performance and cloud computing, computational biology and medicine to discuss the organization of large scale biological and biomedical databases, the parallel/service-based implementation of bioinformatics and biomedical applications, and problems and opportunities of moving biomedical and health applications on the cloud.

The workshop will focus on research issues, problems, and opportunities of moving biomedical and health applications to the cloud, as well as on the opportunity to define guidelines and minimum requirements for a Biomedical Cloud. Moreover, the workshop will discuss parallel and distributed management and analysis of molecular and clinical data, that more and more need to be integrated and analyzed in a joint way.

The main themes and research topics of interest will regard the applications of parallel and high performance computing to biology and medicine, as well as Cloud Computing opportunities and problems for bioinformatics and biomedical applications, including:

- Large scale biological and biomedical databases
- Data integration and ontologies in biology and medicine
- Integration and analysis of molecular and clinical data
- Parallel bioinformatics algorithms
- Parallel visualization and exploration of omics and clinical data
- Parallel visualization and analysis of biomedical images
- Computing environments for large scale collaboration
- Scientific workflows in bioinformatics and biomedicine
- Emerging architectures and programming models for bioinformatics and biomedicine
- Parallel processing of bio-signals
- Modeling and simulation of complex biological processes
- Cloud Computing for bioinformatics and biomedicine
- Cloud Computing for health systems
- Privacy issues for Cloud-based biomedical applications
- (Web) Services for bioinformatics and biomedicine
- Grid Computing for bioinformatics and biomedicine
- Peer-To-Peer Computing for bioinformatics and biomedicine

The workshop will take place on September 21st (tentative).
The workshop is scheduled as a half-day event
in conjunction with the ACM BCB conference.

ParBio 2020 welcomes original submissions that have not been published, and that are not under review by another conference or journal. Papers should not exceed 10 pages in ACM template on 8.5 x 11 inch paper. See ACM Templates:

All submissions will be evaluated on their originality, technical soundness, significance, presentation, and interest to the conference attendees.

Submission implies the willingness of at least one of the authors to register and present the work associated with the paper submitted. ParBio’s technical program committee will review all submitted papers. All accepted papers of registered authors will be included in the workshop proceedings published by ACM digital libraries. Authors of selected papers may be invited to adapt their papers for their publication in several journals. Authors of accepted papers will be required to submit an online ACM Copyright Form. Authors will be contacted by ACM requesting this information. (Note that ACM copyright permissions are directly compatible with NIH and similar open access policies. For more information, see :


We plan to invite selected papers accepted to ParBio-2020 to be submitted for a journal special issue (more details will be posted soon).

Paper submission: July 6, 2020 (Extended)
Notifications sent to authors: July 20, 2020
Camera-ready papers due: July 29, 2020
Workshop: Sept. 21–24, 2020

Giuseppe Agapito, University Magna Graecia of Catanzaro, Italy
Mario Cannataro, University Magna Graecia of Catanzaro, Italy
Wes Lloyd, School of Engineering and Technology, University of Washington - Tacoma, Washington

Pratul K. Agarwal, Oak Ridge National Laboratory, USA
Marian Bubak, AGH Krakow, PL, and University of Amsterdam, NL
Umit Catalyurek, The Ohio State University, USA
Jake Y. Chen, Indiana University - Purdue University Indianapolis (IUPUI), USA
Tim Clark, Harvard Medical School, USA
Giuseppe Di Fatta, University of Reading, UK
Werner Dubitzky, University of Ulster, UK
Ling Hong Hung, University of Washington – Tacoma, USA
Ananth Y. Grama, Purdue University, USA
Concettina Guerra, Georgia Institute of Technology, USA
Kamer Kaya, The Ohio State University, USA
Salvatore Orlando, University of Venezia, Italy
Maria S. Perez, Universidad Politecnica de Madrid, Madrid, Spain
Shruti Ramesh, Micrsosoft, Redmond, WA USA
Richard Sinnott, University of Melbourne, Melbourne, Australia
Roberto Tagliaferri, University of Salerno, Italy
Paolo Trunfio, University of Calabria, Italy
Albert Zomaya, University of Sydney, Australia

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