High Performance Computing for Life and Network Sciences: Mathematical Models, Algorithms, and Tools

Dear Colleagues,

Advancements in Life Sciences are largely driven by the development of powerful technologies and computational tools. Applications range from drug discovery and personalized medical therapies to improved agricultural and green energy production. However, the solution of real-world problems requires a multidisciplinary approach and poses new challenges to the field of High-Performance Computing (HPC) at different levels:

• The mathematical modeling and the simulation of complex phenomena (human organ functions, evolution of diseases, sustainable energy systems, etc.);
• The modelling of such phenomena by using large complex network that should be efficiently analysed

• the processing and analysis of massive amounts of data produced by modern technologies (omics and genome sequencing, functional and anatomical imaging, High-Content Screening, etc.);
• the extracting, merging and understanding of information from different sources (merging different types of images, bridging imaging and omics data, etc.);
• the storage, security, and availability of datasets (in order to gather information, compare results, reproduce the experiments, etc.).

The Special Issue is also connected to the EuroPar worshop: HPC4LifeS2021, and extended versions of the high quality papers selected by program committee and presented on the conference will be recommended for publication.

The HPC4LifeS Workshop is oriented to explore the key role of HPC algorithms, methodologies and tools for solving problems related to different branches of Life Sciences (Biology, Biomedicine, Bioengineering, Network Science, Ecology, etc.).

Topics of interest include, but are not limited to, the following:

• Parallel Computing for Biological Systems
• Parallel Simulations
• Parallel and Distributed Genetic Algorithms
• Parallel and Distributed Algorithms for Network Analysis
• Parallel and Distributed Algorithms for Network Embedding
• Parallel Data Mining Approaches to Life Sciences
• Parallel and Distributed Computing in genomic research
• Parallel and Distributed architecture for Bioengineering
• Cloud Computing for Bioengineering
• Machine Learning techniques for predictive algorithms
• Metabolic and regulatory networks
• Linking variety of databases

Dr. Laura Antonelli
Dr. Pietro Hiram Guzzi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• high scientifc computing
• network analysis and embedding
• high performance machine learning