HiCOMB 2020 - 19th IEEE International Workshop on High Performance Computational Biology

The size and complexity of genome- and proteome-scale data sets and biomedical health data sets continue to grow at a furious pace, and the analysis of these complex, noisy, data sets demands efficient algorithms and high performance computing architectures. Hence, high-performance computing (HPC) has become an integral part of research and development in bioinformatics, computational biology, and medical and health informatics. The goal of the HiCOMB workshop is to showcase novel HPC research and technologies to solve data- and compute-intensive problems arising from all areas of computational life sciences. The workshop will feature contributed papers as well as invited talks from reputed researchers in the field.
For peer-reviewed papers, we invite authors to submit original and previously unpublished work that are at the intersection of the "pillars" of modern day computational life sciences and HPC. More specifically, we encourage submissions from all areas of biology that can benefit from HPC, and from all areas of HPC that need new development to address the class of computational problems that originate from biology.
Areas of interest within computational life sciences include (but not limited to):
Biological sequence analysis (genome assembly, long/short read data structures, read mapping, clustering, variant analysis, error correction, genome annotation)
Computational structural biology (protein structure, RNA structure)
Functional genomics (transcriptomics, RNAseq/microarrays, proteomics)
Systems biology and networks (biological network analysis, gene regulatory networks, metabolomics, molecular pathways)
Tools for integrated multi-omics and biological databases (network construction, modeling, link inference)
Phylogeny (phylogenetic tree reconstruction, molecular evolution)
Microbes and microbiomes (taxonomical binning, classification, clustering, annotation)
Biomedical health analytics and biomedical imaging (electronic health records, precision medicine, image analysis)
Biomedical literature mining (text mining, ontology, natural language processing)
Computational epidemiology (infectious diseases, diffusion mechanisms)
Phenomics and precision agriculture (IoT technologies, feature extraction)
Areas of interest within HPC include (but are not limited to):
Parallel and distributed algorithms (scalable machine learning, parallel graph/sequence analytics, combinatorial pattern matching, optimization, parallel data structures, compression)
Data-intensive computing techniques (communication-avoiding/synchronization-reducing techniques, locality-preserving techniques, big data streaming techniques)
Parallel architectures (multicore, manycore, CPU/GPU, FPGA, system-on-chip, hardware accelerators, energy-aware architectures, hardware/software co-design)
Memory and storage technologies (processing-in-memory, NVRAM, burst buffers, 3D RAM, parallel/distributed I/O)
Parallel programming models (libraries, domain specific languages, compiler/runtime systems)
Scientific workflows (data management, data wrangling, automated workflows, productivity)
Empirical evaluations (performance modeling, case-studies)