Edited by Sunita Chandrasekaran and Eric Stahlberg
Volume 19 Supplement 18
Computational Approaches for Cancer at SC17
Research
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
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Citation: BMC Bioinformatics 2018 19(Suppl 18):487
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CAT: computer aided triage improving upon the Bayes risk through ε-refusal triage rules
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...
Citation: BMC Bioinformatics 2018 19(Suppl 18):485 -
Sparse coding of pathology slides compared to transfer learning with deep neural networks
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...
Citation: BMC Bioinformatics 2018 19(Suppl 18):489 -
Real-time data analysis for medical diagnosis using FPGA-accelerated neural networks
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...
Citation: BMC Bioinformatics 2018 19(Suppl 18):490 -
High-throughput binding affinity calculations at extreme scales
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,...
Citation: BMC Bioinformatics 2018 19(Suppl 18):482 -
Deep clustering of protein folding simulations
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...
Citation: BMC Bioinformatics 2018 19(Suppl 18):484 -
CANDLE/Supervisor: a workflow framework for machine learning applied to cancer research
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...
Citation: BMC Bioinformatics 2018 19(Suppl 18):491 -
Predicting tumor cell line response to drug pairs with deep learning
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.
Citation: BMC Bioinformatics 2018 19(Suppl 18):486 -
High-throughput cancer hypothesis testing with an integrated PhysiCell-EMEWS workflow
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...
Citation: BMC Bioinformatics 2018 19(Suppl 18):483 -
Scalable deep text comprehension for Cancer surveillance on high-performance computing
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...
Citation: BMC Bioinformatics 2018 19(Suppl 18):488
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