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Related Experiment Video

Updated: Jan 5, 2026

High-resolution Single Particle Analysis from Electron Cryo-microscopy Images Using SPHIRE

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Published on: May 16, 2017

AI Meets Exascale Computing: Advancing Cancer Research With Large-Scale High Performance Computing.

Tanmoy Bhattacharya¹, Thomas Brettin², James H Doroshow³

¹Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, United States.

Frontiers in Oncology

|October 22, 2019

Summary

Data science, AI, and advanced computing accelerate cancer research by integrating diverse data and ML models. The NCI-DOE JDACS4C collaboration tackles challenges in data sharing and AI scalability for better cancer understanding and treatment.

Keywords:

artificial intelligence cancer research deep learning high performance computing multi-scale modeling natural language processing precision medicine uncertainty quantification

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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

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Published on: May 17, 2019

Area of Science:

Computational biology and bioinformatics
Artificial intelligence in oncology
High-performance computing for biomedical research

Background:

Major advances in data availability, AI/ML algorithms, and computing power are transforming cancer research.
In silico ML models offer insights from diverse data types like omics, imaging, and clinical texts.
Significant challenges remain in data integration, AI scalability, and model reliability for cancer research.

Purpose of the Study:

To review the National Cancer Institute (NCI)-Department of Energy (DOE) collaboration, Joint Design of Advanced Computing Solutions for Cancer (JDACS4C).
To highlight how this collaboration advances computing and data technologies to accelerate cancer research.
To demonstrate the integration of data, computing resources, and ML models across molecular, cellular, and population levels.

Main Methods:

Leveraging diverse biomedical data (genomics, imaging, clinical notes).
Utilizing advanced Artificial Intelligence (AI) and Machine Learning (ML) algorithms.
Employing pre-exascale high-performance computing resources.

Main Results:

Building in silico ML models for transformative insights in cancer research.
Addressing challenges in multimodal data integration and AI model scalability.
Accelerating understanding of cancer biology, treatment options, and patient outcomes.

Conclusions:

The JDACS4C collaboration effectively integrates data, computing, and ML to advance cancer research.
This integrated approach aids in understanding basic cancer biology and developing new treatments.
The initiative aims to improve prediction of outcomes and personalize cancer treatments for patients.