3rd International Workshop on High Performance Computing, Big Data Analytics and Integration for Multi-Omics Biomedical Data
In conjunction with ACM-BCB 2021
August 1 – 4, 2021 (Virtual)
HPC-BOD 2021 Call For Papers
Enormous amounts of data are being produced using modern technologies such as Next Generation Sequencing machines and high-throughput Mass Spectrometers. The availability of such large and heterogeneous datasets creates challenges in terms of storage, transmission, computations and integration of these Big Data sets. In order to process such data in a timely manner, big data analytics techniques and high performance computing (HPC) is becoming an essential component in system biology, bioinformatics and computational biology.
The goal of this workshop is to provide a forum for big data analytics and high-performance computing professionals and academics alike to discuss latest research in HPC solutions to these compute-intensive and data-intensive problems. We are especially interested in high-performance computing algorithms, and big data analytic techniques for integration of large-scale high-throughput multi-omics data sets.
The workshop will feature submitted papers as well as invited papers and talks from reputed researchers in the field of big data analytics, high-performance computing and computational biology.
Paper submission link: https://easychair.org/my/conference?conf=hpcbod2021#
Selected papers will possibly be published in Plos Special Collections (web-link) and in the Journal of Computational Biology and Bioinformatics (JBCB) (web-link). Papers from HPC-BOD 2020 were invited and are being reviewed by PLos Special Collections.
Thrust 1: Integration of Big Omics Data.
Areas of interest within computational life sciences include (but not limited to):
Computational genomics and metagenomics
Genome assembly, long/short read data structures, read mapping, clustering, variant analysis, error correction, genome annotation, and other computational problems in large-scale genomics
Computational proteomics and proteogenomics
Peptide identification from Big Mass Spectrometry data including database search and de novo methods, Genome annotations via mass spectrometry, Identification of post-translational modifications, Structural genomics via mass spectrometry, Protein-protein interactions and other computational problems in large-scale proteomics
Network biology methods for multi-omics integration
Network representation learning (e.g., deep learning on graphs and network embedding), higher-order network analysis (network motifs/graphlets and networks of networks), inference of biological networks (e.g., gene regulatory networks, competing endogenous RNA networks, link prediction), comparison of biological networks (network alignment analysis and alignment-free network comparison), network-based personalized medicine (e.g., drug response prediction and drug target prediction)
Computational Neuroinformatics and Connectomics
Standardization in multiscale and multimodal modeling, Computational infrastructure for neuroscience: automation / pipelines, Machine learning in neuroscience, Reproducible neuroscience + open science
Other Omics and Integration for Systems Biology
Other computational problems in omics including but not limited to Epigenomics, Lipidomics, Glycomics, Foodomics, Transcriptomics, Metabolomics and integration of these omics datasets to get systems biology insights are also encouraged to submit.
Thrust 2: High-Performance Computing for Big Data Omics.
Areas of interest within HPC include (but are not limited to):
Parallel and distributed algorithms for big data Omics
Scalable machine learning, parallel graph/sequence analytics, combinatorial pattern matching, optimization, parallel data structures, compression/decompression, multicore, manycore, CPU/GPU, FPGA, system-on-chip, hardware accelerators, energy-aware architectures, hardware/software co-design
Accessible Scientific workflows for Big Data Omics
Data management, Data wrangling, Automated workflows, Cloud-enabled solutions for computational biology, and Energy-aware High-Performance Biological Applications
Big Data Omics Analytics, Infrastructure, and Management
Novel techniques to deal with big omics data including but not limited to sketching, sampling, streaming, compression/decompression, succinct data-structures and algorithms, novel encoding techniques, efficient methods to integrate multiomics data and Multimedia and Multi-structured Omics data
Hardware Acceleration for Big Omics Data
FPGA/CGRA/GPU accelerators for Big Data applications, Domain-specific and heterogeneous architectures, and design that can accelerate machine-learning aspects of dealing with big omics data.
Submitted manuscripts should not exceed 10 pages in ACM “sigconf” template on 8.5 x 11 inch paper (http://www.acm.org/publications/proceedings-template). HPC-BOD technical program committee will review all submissions on the basis of their originality, technical soundness, significance, presentation, and relevance to the conference attendees.
The submission link is: https://easychair.org/my/conference?conf=hpcbod2021#
All submissions will be peer-reviewed by members from the program committee using a single-blind review process. At the time of submission, the author list should be final. Any subsequent changes to the author list post-submission needs to be done with the approval of the program chairs.
Three kind of papers can be submitted to HPC-BOD:
- Full Papers (10 pages)
- Short papers (4 pages)
- Extended abstract (1 page)
Acceptance of any of these would mean an oral presentation. However, the authors would have to pay registration for the paper to be included in the ACM-BCB proceedings.
Due date for full workshop papers submission: June 9th 2021
June 5th, 2021
Notification of paper acceptance to authors: June 12th 2021
June 10th, 2021
Camera-ready of accepted papers: June 15th, 2021