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 ever-increasing production of experimental and clinical data.

Considering the complex analysis pipelines often used in biomedical research, there is a main bottleneck that involves the storage, integration, and analysis of experimental data, as well as their correlation and integration with publicly available data banks.

While parallel 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, to reduce the cost of the data analysis task, and especially to change the overall model of biomedical research and health provision.

High-performance infrastructures may offer the huge data storage needed to store experimental and biomedical data, while parallel computing can be used for basic pre-processing (e.g. parallel BLAST, mpiBLAST) and for more advanced analysis (e.g. parallel data mining). In such a scenario, novel parallel architectures (e.g. CELL processors, GPUs, FPGA, 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.

On the other hand, these technologies yet 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, and prohibitive costs (different professional-level applications in the biomedical domain have an high starting cost that prevent many small laboratories to use them).

The Cloud Computing technology, and its variations such as Serverless Computing, that is able to offer scalable costs and increased reachability, availability and easiness of application use, and possibility to enforce collaboration among scientists, is already changing the business model in different sectors and now it has begun to be 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.

Edge Computing is another recent paradigm that starts to be used in bioinformatics and machine learning. The workshop will also consider bioinformatics and biomedical algorithms and applications developed using Edge Computing.

In line with these technological advancements and in addition to the historical themes of the workshop, this year significant emphasis will be placed on the integration of artificial intelligence in bioinformatics and biomedicine. The investigation of the potential of natural language processing (NLP) and large language models (LLMs) to enhance biomedical research and clinical applications deserves significant attention. Furthermore, sustainable computing practices can ensure that AI advancements are environmentally and economically viable. A human-centered approach is also essential, focusing on designing and implementing AI solutions that meet the needs of patients, healthcare providers, and researchers, ensuring ethical considerations and user-centric solutions.

Objectives and Goals:

The goal of ParBio is to bring together scientists in the fields of high performance computing, computational biology and medicine to discuss, among the others, the parallel implementation of bioinformatics and biomedical applications and problems and opportunities of moving biomedical and health applications on the cloud or on the edge. Moreover, artificial intelligence, LLMs, machine learning and big data analytics issues in healthcare and bioinformatics will be addressed. Finally, 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.

Submission Guidelines

ParBio welcomes original submissions that have not been published and that are not under review by another conference or journal. You can submit a Full Paper (8-10 pages) or a Short Paper (4-5 pages). Submitted manuscripts should comply with the requirements delineated on the ACM Master Article Template website (https://www.acm.org/publications/proceedings-template) on 8.5 x 11 inch paper. 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, see http://authors.acm.org/main.html for more information). 

Authors should submit papers using the ParBio2024 Easy Chair Installation: https://easychair.org/conferences/?conf=parbio2024.

Committees

Program Committee (To Be Confirmed)

• -    Pratul K. Agarwal, Oak Ridge National Laboratory, USA
• -    Luca Barillaro, University Magna Graecia of Catanzaro, Italy
• -    Lorella Bottino, University Magna Graecia of Catanzaro, Italy
• -    Umit Catalyurek, The Ohio State University, USA
• -    Jake Y. Chen, Indiana University - Purdue University Indianapolis (IUPUI), USA
• -    Pietro Cinaglia, University Magna Graecia of Catanzaro, Italy
• -    Tim Clark, Harvard Medical School, USA
• -    Werner Dubitzky, University of Ulster, UK
• -    Ling Hong Hung, University of Washington – Tacoma, USA
• -    Ananth Y. Grama, Purdue University, USA
• -    Kamer Kaya, The Ohio State University, USA
• -    Ilaria Lazzaro, University Magna Graecia of Catanzaro, Italy
• -    Maria Chiara Martinis, University Magna Graecia of Catanzaro, Italy
• -    Marianna Milano, University Magna Graecia of Catanzaro, Italy
• -    Salvatore Orlando, University of Venezia, Italy
• -    Maria S. Perez, Universidad Politecnica de Madrid, Madrid, Spain
• -    Shruti Ramesh, Micrsosoft, Redmond, WA USA
• -    Ileana Scarpino, University Magna Graecia of Catanzaro, Italy
• -    Marzia Settino, University Magna Graecia of Catanzaro, Italy
• -    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
• -    Chiara Zucco, University Magna Graecia of Catanzaro, Italy

Organizing committee

• 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

• Chiara Zucco, University Magna Graecia of Catanzaro, Italy

Publication

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, see http://authors.acm.org/main.html for more information). 

Venue

The workshop will be held in Shenzhen, China, on November 22-25, 2024 (see https://acm-bcb.org/)

Contact

All questions about submissions should be emailed to Prof. Mario Cannataro (cannataro AT unicz DOT it)