NCI-DOE Collaboration Publications

The Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) program is the focal point of the strategic, interagency collaboration between the National Cancer Institute (NCI) and the US Department of Energy (DOE) to simultaneously accelerate advances in precision oncology and scientific computing.

Based on a multidisciplinary, team science approach, JDACS4C’s three research pilots were co-designed by NCI and DOE; align with several existing NCI and DOE programs; and are jointly led by DOE and NCI supported scientists. These teams include scientists from NCI and Frederick National Laboratory for Cancer Research and experts from DOE national laboratories: principally Argonne, Lawrence Livermore, Los Alamos, and Oak Ridge.

Below is a list of the publications. For more information on the NCI-DOE Collaboration, visit the JDACS4C page. 

 

Publications as of April 15, 2021:

 

2021, Limitations of Transformers on Clinical Text Classification, IEEE Journal of Biomedical and Health Informatics

2021, Deep Active Learning for Classifying Cancer Pathology Reports, BMC Bioinformatics Pilot 3 Capabilities: Active Learning

2021, A Pre-Training and Self-Training Approach for Biomedical Named Entity Recognition, PLOS ONE

2020, DeepFreeze: Towards Scalable Asynchronous Checkpointing of Deep Learning Models, 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing

2020, Distributed Bayesian Optimization of Deep Reinforcement Learning Algorithms, Journal of Parallel and Distributed Computing

2020, Generation and Evaluation of Synthetic Patient Data, BMC Medical Research Methodology

2020, How Anionic Lipids Affect Spatiotemporal Properties of KRAS4B on Model Membranes, The Journal of Physical Chemistry B

2020, Knowledge Graph-Enabled Cancer Data Analytics, IEEE Transactions on Emerging Topics in Computing

2020, Presence or Absence of Ras Dimerization Shows Distinct Kinetic Signature in Ras-Raf Interaction, Biophysical Journal

2020, Privacy-Preserving Deep Learning NLP Models for Cancer Registries, IEEE Transactions on Emerging Topics in Computing

2020, Selective Information Extraction Strategies for Cancer Pathology Reports with Convolutional Neural Networks, Recent Advances in Big Data and Deep Learning

2020, The Plasma Membrane as a Competitive Inhibitor and Positive Allosteric Modulator of KRas4B Signaling, Biophysical Journal

2020, Using Case-level Context to Classify Cancer Pathology Reports, PLOS One

2019, A Knowledge Graph Approach for the Secondary Use of Cancer Registry Data, 2019 IEEE EMBS International Conference on Biomedical & Health Informatics

2019, A Massively Parallel Infrastructure for Adaptive Multiscale Simulations: Modeling RAS Initiation Pathway for Cancer, Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

2019, Adversarial Training for Privacy-Preserving Deep Learning Model Distribution, 2019 IEEE International Conference on Big Data (Big Data)

2019, AI Meets Exascale Computing: Advancing Cancer Research With Large-Scale High Performance Computing, Frontiers in Oncology

2019, Autoencoder Node Saliency: Selecting Relevant Latent Representations, Pattern Recognition

2019, Classifying Cancer Pathology Reports with Hierarchical Self-attention Networks, Artificial Intelligence in Medicine

2019, Combating Label Noise in Deep Learning Using Abstention, Proceedings of the 36th International Conference on Machine Learning

2019, Computationally Efficient Learning of Quality Controlled Word Embeddings for Natural Language Processing, 2019 IEEE Computer Society Annual Symposium on VLSI

2019, Computing Long Time Scale Biomolecular Dynamics Using Quasi-stationary Distribution Kinetic Monte Carlo (QSD-KMC), The Journal of Chemical Physics

2019, Deep Transfer Learning Across Cancer Registries for Information Extraction from Pathology Reports, 2019 IEEE EMBS International Conference on Biomedical & Health Informatics

2019, Extraction of Tumor Site from Cancer Pathology Reports using Deep Filters, "Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics - BCB '19"

2019, Information Extraction from Cancer Pathology Reports with Graph Convolution Networks for Natural Language Texts, 2019 IEEE International Conference on Big Data 

2019, Inverse Regression for Extraction of Tumor Site from Cancer Pathology Reports, 2019 IEEE EMBS International Conference on Biomedical & Health Informatics

2019, Model-based Hyperparameter Optimization of Convolutional Neural Networks for Information Extraction from Cancer Pathology Reports on HPC, 2019 IEEE EMBS International Conference on Biomedical & Health Informatics

2019, Modeling Cell Line-specific Recruitment of Signaling Proteins to the Insulin-like Growth Factor 1 Receptor, PLOS Computational Biology

2019, New Insights into RAS Biology Reinvigorate Interest in Mathematical Modeling of RAS Signaling, Seminars in Cancer Biology

2019, On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks, Advances in Neural Information Processing Systems 32

2019, Scalable Reinforcement-learning-based Neural Architecture Search for Cancer Deep Learning Research, Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

2019, The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making, Nature Machine Intelligence

2019, Unsupervised Machine Learning for Analysis of Phase Separation in Ternary Lipid Mixture, Journal of Chemical Theory and Computation

2018, CANDLE/Supervisor: A Workflow Framework for Machine Learning Applied to Cancer Research, BMC Bioinformatics

2018, Capturing Phase Behavior of Ternary Lipid Mixtures with a Refined Martini Coarse-Grained Force Field, Journal of Chemical Theory and Computation

2018, CAT: Computer Aided Triage Improving Upon the Bayes Risk Through Ε-Refusal Triage Rules, BMC Bioinformatics

2018, Coarse-to-Fine Multi-Task Training of Convolutional Neural Networks for Automated Information Extraction from Cancer Pathology Reports, 2018 IEEE EMBS International Conference on Biomedical & Health Informatics

2018, Deep Learning for Automated Extraction of Primary Sites from Cancer Pathology Reports, IEEE Journal of Biomedical and Health Informatics

2018, Dissecting RAF Inhibitor Resistance by Structure-Based Modeling Reveals Ways to Overcome Oncogenic RAS Signaling, Cell Systems

2018, Filter Pruning of Convolutional Neural Networks for Text Classification: A Case Study of Cancer Pathology Report Comprehension, 2018 IEEE EMBS International Conference on Biomedical & Health Informatics

2018, Hierarchical Attention Networks for Information Extraction from Cancer Pathology Reports, Journal of the American Medical Informatics Association

2018, Methionine 170 is an Environmentally Sensitive Membrane Anchor In The Disordered HVR of K-Ras4B, The Journal of Physical Chemistry B

2018, Molecular Recognition of RAS/RAF Complex at the Membrane: Role of RAF Cysteine-Rich Domain, Scientific Reports

2018, Portable and Reusable Deep Learning Infrastructure with Containers to Accelerate Cancer Studies, 2018 IEEE/ACM 4th International Workshop on Extreme Scale Programming Models and Middleware (ESPM2)

2018, Predicting Tumor Cell Line Response to Drug Pairs with Deep Learning, BMC Bioinformatics

2018, Retrofitting Word Embeddings with the UMLS Metathesaurus for Clinical Information Extraction, 2018 IEEE International Conference on Big Data

2018, Scalable Deep Text Comprehension for Cancer Surveillance on High-Performance Computing, BMC Bioinformatics

2018, Sparse Coding of Pathology Slides Compared to Transfer Learning with Deep Neural Networks, BMC Bioinformatics

2017, Computational Lipidomics of the Neuronal Plasma Membrane, Biophysical Journal

2017, Energy Efficient Stochastic-Based Deep Spiking Neural Networks for Sparse Datasets, 2017 IEEE International Conference on Big Data

2017, Multi-Task Deep Neural Networks for Automated Extraction of Primary Site and Laterality Information from Cancer Pathology Reports, Advances in Big Data

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