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Volume 19 Supplement 18

Computational Approaches for Cancer at SC17


Publication of this supplement has not been supported by sponsorship. Information about the source of funding for publication charges can be found in the individual articles. The articles have undergone the journal's standard peer review process for supplements. ES is the Frederick National Laboratory for Cancer Research co-program lead for the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) program. Several of the papers presented in the workshop and included in the supplement are the result of work supported by the JDACS4C program and, as such, ES is listed as a co-author on one of the articles in the supplement but was not involved in its review. SC declares no competing interests.

Denver, CO, USA17 November 2017

Conference website

Edited by Sunita Chandrasekaran and Eric Stahlberg

  1. Manual extraction of information from electronic pathology (epath) reports to populate the Surveillance, Epidemiology, and End Result (SEER) database is labor intensive. Systematizing the data extraction autom...

    Authors: Nicolas Hengartner, Leticia Cuellar, Xiao-Cheng Wu, Georgia Tourassi, John Qiu, Blair Christian and Tanmoy Bhattacharya
    Citation: BMC Bioinformatics 2018 19(Suppl 18):485
  2. Histopathology images of tumor biopsies present unique challenges for applying machine learning to the diagnosis and treatment of cancer. The pathology slides are high resolution, often exceeding 1GB, have non...

    Authors: Will Fischer, Sanketh S. Moudgalya, Judith D. Cohn, Nga T. T. Nguyen and Garrett T. Kenyon
    Citation: BMC Bioinformatics 2018 19(Suppl 18):489
  3. Real-time analysis of patient data during medical procedures can provide vital diagnostic feedback that significantly improves chances of success. With sensors becoming increasingly fast, frameworks such as De...

    Authors: Ahmed Sanaullah, Chen Yang, Yuri Alexeev, Kazutomo Yoshii and Martin C. Herbordt
    Citation: BMC Bioinformatics 2018 19(Suppl 18):490
  4. Resistance to chemotherapy and molecularly targeted therapies is a major factor in limiting the effectiveness of cancer treatment. In many cases, resistance can be linked to genetic changes in target proteins,...

    Authors: Jumana Dakka, Matteo Turilli, David W. Wright, Stefan J. Zasada, Vivek Balasubramanian, Shunzhou Wan, Peter V. Coveney and Shantenu Jha
    Citation: BMC Bioinformatics 2018 19(Suppl 18):482
  5. We examine the problem of clustering biomolecular simulations using deep learning techniques. Since biomolecular simulation datasets are inherently high dimensional, it is often necessary to build low dimensio...

    Authors: Debsindhu Bhowmik, Shang Gao, Michael T. Young and Arvind Ramanathan
    Citation: BMC Bioinformatics 2018 19(Suppl 18):484
  6. Current multi-petaflop supercomputers are powerful systems, but present challenges when faced with problems requiring large machine learning workflows. Complex algorithms running at system scale, often with di...

    Authors: Justin M. Wozniak, Rajeev Jain, Prasanna Balaprakash, Jonathan Ozik, Nicholson T. Collier, John Bauer, Fangfang Xia, Thomas Brettin, Rick Stevens, Jamaludin Mohd-Yusof, Cristina Garcia Cardona, Brian Van Essen and Matthew Baughman
    Citation: BMC Bioinformatics 2018 19(Suppl 18):491
  7. The National Cancer Institute drug pair screening effort against 60 well-characterized human tumor cell lines (NCI-60) presents an unprecedented resource for modeling combinational drug activity.

    Authors: Fangfang Xia, Maulik Shukla, Thomas Brettin, Cristina Garcia-Cardona, Judith Cohn, Jonathan E. Allen, Sergei Maslov, Susan L. Holbeck, James H. Doroshow, Yvonne A. Evrard, Eric A. Stahlberg and Rick L. Stevens
    Citation: BMC Bioinformatics 2018 19(Suppl 18):486
  8. Cancer is a complex, multiscale dynamical system, with interactions between tumor cells and non-cancerous host systems. Therapies act on this combined cancer-host system, sometimes with unexpected results. Sys...

    Authors: Jonathan Ozik, Nicholson Collier, Justin M. Wozniak, Charles Macal, Chase Cockrell, Samuel H. Friedman, Ahmadreza Ghaffarizadeh, Randy Heiland, Gary An and Paul Macklin
    Citation: BMC Bioinformatics 2018 19(Suppl 18):483
  9. Deep Learning (DL) has advanced the state-of-the-art capabilities in bioinformatics applications which has resulted in trends of increasingly sophisticated and computationally demanding models trained by large...

    Authors: John X. Qiu, Hong-Jun Yoon, Kshitij Srivastava, Thomas P. Watson, J. Blair Christian, Arvind Ramanathan, Xiao C. Wu, Paul A. Fearn and Georgia D. Tourassi
    Citation: BMC Bioinformatics 2018 19(Suppl 18):488

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