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 pipeline of the biomedical research, the bottleneck is more and more moving toward the storage, integration, and analysis of experimental data, as well as their correlation and integration with publicly available datasets. While Parallel Computing and Grid Computing may offer the computational power and storage capacity 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.
Grid infrastructures may offer the data storage needed to store the huge 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 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 non-scalable costs (different professional-level applications in the biomedical domain have high starting costs that prevent many small laboratories to use them).
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 ParBio2021 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.

TOPICS OF INTEREST
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

PROGRAM
The workshop will take place on August 7, 2022 (Schedule To Be Announced). The program is not available yet.

PAPER SUBMISSION, REGISTRATION AND PUBLICATION
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 (6-10 pages) or a Short Paper (1-4 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 ParBio2022 Easy Chair Installation: https://easychair.org/conferences/?conf=parbio2022.

IMPORTANT DATES
(all submissions close 11:59pm Anywhere On Earth [AOE])
Paper submissions due: June 6, 2022
Notifications sent to authors: June 20, 2022
Camera-ready papers due: June 27, 2022

JOURNAL SPECIAL ISSUE
After of the workshop, we plan to invite the best papers of the workshop for a special issue of an international journal.