Template:Article of the week

From LIMSWiki
Revision as of 15:47, 8 January 2020 by Shawndouglas (Talk | contribs) (Updated article of the week text)

Jump to: navigation, search
Fig1 Bhattacharya FrontInOnc2019 9.jpg

"AI meets exascale computing: Advancing cancer research with large-scale high-performance computing"

The application of data science in cancer research has been boosted by major advances in three primary areas: (1) data: diversity, amount, and availability of biomedical data; (2) advances in artificial intelligence (AI) and machine learning (ML) algorithms that enable learning from complex, large-scale data; and (3) advances in computer architectures allowing unprecedented acceleration of simulation and machine learning algorithms. These advances help build in silico ML models that can provide transformative insights from data, including molecular dynamics simulations, next-generation sequencing, omics, imaging, and unstructured clinical text documents. Unique challenges persist, however, in building ML models related to cancer, including: (1) access, sharing, labeling, and integration of multimodal and multi-institutional data across different cancer types; (2) developing AI models for cancer research capable of scaling on next-generation high-performance computers; and (3) assessing robustness and reliability in the AI models. (Full article...)

Recently featured:

Building infrastructure for African human genomic data management
Process variation detection using missing data in a multihospital community practice anatomic pathology laboratory
Development and validation of a fast gas chromatography–mass spectrometry method for the determination of cannabinoids in Cannabis sativa L