Authors: Yury Khudyakov[1], Ion Mandoiu[2], Pavel Skums[3], Alex Zelikovsky[3], [1]Centers for Disease Control and Prevention, [2]University of Connecticut, [3]Georgia State University

Abstract: The CAME workshop provides a forum for presentation and discussion of the latest computational research in molecular epidemiology. This multidisciplinary workshop will bring together field practitioners of molecular epidemiology, molecular evolutionists, population geneticists, medical researchers, bioinformaticians, statisticians and computer scientists interested in the latest developments in algorithms, mining, visualization, modeling, simulation and other methods of computational, statistical and mathematical analysis of genetic and molecular data in the epidemiological context.
Molecular epidemiology is essentially an integrative scientific discipline that considers molecular biological processes in specific epidemiological settings. It relates molecular biological events to etiology, distribution and prevention of disease in human populations. Over years, molecular epidemiology became extensively fused with mathematical and computational science and immensely benefited from this tight association. The workshop will review the latest advancements in application of mathematical and computational approaches to molecular epidemiology.

Tutorial url: http://alan.cs.gsu.edu/came18/
Submission url: Submission by invitation only. Contact organizers

Authors: Dr. Byung-Jun Yoon[1], Dr. Xiaoning Qian[1], Dr. Tamer Kahveci [2], Dr. Ranadip Pal[3], [1]Texas A&M University, Dept. Electrical & Computer Engineering , [2]University of Florida, Dept. Computer and Information Science and Engineering , [3]Texas Tech University, Electrical and Computer Engineering

Abstract: Next-generation high-throughput profiling technologies have enabled more systematic and comprehensive studies of living systems. Network models play crucial roles in understanding the complex interactions that govern biological systems, and their interactions with external environment. The inference and analysis of such complex networks and network-based analysis of large-scale measurement data have already shown strong potential for unveiling the key mechanisms of complex diseases as well as for designing improved therapeutic strategies. At the same time, the inference and analysis of complex biological networks pose new exciting challenges for computer science, signal processing, control, and statistics. We propose to organize the Fifth International Workshop on Computational Network Biology: Modeling, Analysis, and Control (CNB-MAC 2018) in conjunction with ACM-BCB 2018. The previous CNB-MAC workshops have been successfully held in conjunction with ACM-BCB 2014, ACM-BCB 2015, ACM-BCB 2016, ACM- BCB 2017, attracting a fair number of researchers interested in computational network biology.
The workshop aims to provide an international scientific forum for presenting recent advances in computational network biology that involve modeling, analysis, and control of biological systems under different conditions, and system-oriented analysis of large-scale OMICS data. The proposed full-day workshop will solicit (i) highlights that present advances in the field that have been reported in recent journal publications, (ii) extended abstracts for poster presentation at the workshop, which will provide an excellent venue for quick dissemination of the latest research results in computational network biology, and (iii) original research papers that report new research findings that have not been published elsewhere. Full length original research papers accepted for presentation at the workshop will be published in a supplement issue in partner journals that will be identified after the workshop proposal is accepted. The first and the second CNB-MAC workshops have partnered with EURASIP Journal on Bioinformatics and Systems Biology, and the third CNB-MAC workshop partnered with BMC Bioinformatics, BMC Systems Biology, and BMC Genomics. The fourth CNB-MAC workshop partnered with BMC Bioinformatics, BMC Systems Biology, BMC Genomics, and IET Systems Biology. The main emphasis of the proposed workshop will be on rigorous mathematical or computational approaches in studying biological networks, analyzing large-scale OMICS data, and investigating mathematical models for human-microbiome- environment interactions.

Workshop url: https://cnbmac.org/
Submission url: https://www.easychair.org/conferences/?conf=cnbmac2018

Authors: Filip Jagodzinski[1], [2]Brian Chen, [3]Kevin Molloy, [1]Dept. of Computer Science, Western Washington University, [2]Dept. of Computer Science and Engineering, Lehigh University, [3]Dept. of Computer Science, James Madison University

Abstract: The unique nature of protein and nucleotide structures has presented many computational challenges and opportunities. The fast accumulation and use of various sources of data has enabled a variety of novel analysis approaches and computational techniques, yielding a variety of insights and increased understanding of different biophysical phenomena and processes. This workshop aims to provide a dissemination and discussion forum for computational approaches related to protein structural discovery and analysis.
We have successfully hosted the CSBW with BIBM in 2007-2009, 2011, 2012, and 2015, and with ACM- BCB in 2013, 2014, 2016 and 2017. Past workshops have been well attended, with approximately 12 oral presentations for peer reviewed submissions. In past years, we have also held a poster session during the workshop, to complement the conference poster session, and to encourage participation by undergraduate and graduate students in presenting & discussing on-going projects.

Workshop url: http://www.cs.odu.edu/~bioinfo/csbw.html
Submission url: https://easychair.org/conferences/?conf=csbw2018

Authors: KanakaDurga Addepalli[1], Hsinyi (Steve) Tsang[1], [1]National Cancer Institute

Abstract: Technological advancements have given us the ability to sequence genomes in great depths, and, consequently, generated an exponential growth in data. National Cancer Institute Cloud Resources (NCICR)​ , formerly the NCI Cancer Genomics Cloud Pilots, were developed with a goal of enhancing the utility of cancer genomic data and facilitating analysis by co-locating cloud computing and petabyte-scale data. Based on commercial cloud architectures, the Cloud Resources offer the flexibility for users to utilize tools in the form of Docker containers, and tools can be joined to form complex workflows described by Common Workflow Language (CWL) or Workflow Description Language (WDL). The utility and application of the Cloud Resources has been expanded from cancer genomics to include microbe analysis, proteomics, imaging and other “omics” in the future. The cloud environment has proven to be a cost-effective, reproducible, reusable, interoperable, and user-friendly alternative to high-performance computing, with minimal overhead and setup requirements. These production-ready and highly scalable platforms represent a necessary step in a publicly available toolset meant to support open and Findable, Accessible, Interoperable, Reusable (FAIR) scientific research.
Through this demonstration workshop, participants will have the opportunity to

1. learn about the basic features of the NCICR
2. create interoperable, containerized tools, and
3. run genomic analysis on the three cloud-based platforms.

Workshop url: https://stevetsa.github.io/post/acmbcb-ncicr/
Submission url: Not applicable

Authors: Giuseppe Agapito[1], Wes Lloyd[2], [1]Department of Medical and Surgical Sciences, University Magna Græcia of Catanzaro, Italy, [2]Institute of Technology, University of Washington - Tacoma

Abstract: 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, the bottleneck increasingly involves the storage, integration, and analysis of experimental data, as well as their correlation and integration with publicly available data banks.
Grid 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 a high starting cost that prevent many small laboratories to use them).
While parallel computing and Grid computing offer computational power and storage to address the overwhelming availability of data, Cloud Computing additionally hides the complexity of computing infrastructures, while also reducing the cost of data analysis tasks, demonstrating potential to transform the overall model of biomedical research and health related data science.
Cloud Computing, that offers scalable costs, increased accessibility, availability, and ease of application use while enabling potential collaboration among scientists, is already changing the computing business models in different sectors, and recently has been adopted to support 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, and the execution and billing of biomedical applications.

Workshop url: http://staff.icar.cnr.it/cannataro/parbio2018/
Submission url: https://easychair.org/conferences/?conf=parbio2018

Authors: Majid Rastegar-Mojarad[1], Sijia Liu[1], Yanshan Wang[1], Naveed Afzal[1], Liwei Wang[1], Feichen Shen[1], Sunyang Fu[1], Hongfang Liu[1], [1]Department of Health Sciences Research, Mayo Clinic,

Abstract: The application of Natural Language Processing (NLP) methods and resources to clinical and biomedical text has received growing attention over the past years, but progress has been limited by difficulties to access shared tools and resources, partially caused by patient privacy and data confidentiality constraints. Efforts to increase sharing and interoperability of the few existing resources are needed to facilitate the progress observed in the general NLP domain. Leveraging our research in corpus analysis and de-identification research, we have created multiple synthetic data sets for a couple of NLP tasks based on real clinical sentences. We are organizing a challenge workshop to promote community efforts towards the advancement in clinical NLP. The challenge workshop will have two tasks:

• Task 1. Family History Information Extraction -contact Majid Rastegar-Mojarad (mojarad.majid@mayo.edu) or Sijia Liu (liu.sijia@mayo.edu)
• Task 2. Clinical Semantic Textual Similarity -contact Yanshan Wang (wang.yanshan@mayo.edu)

Workshop url: https://groups.google.com/forum/#!forum/ohnlp2018
Submission url: Not applicable