HPC and Machine Learning for Molecular Biology
Frederick H. Streitz, Centers for Disease Control and Prevention; Lawrence Livermore National Laboratory
The combination of high-performance computing (HPC) and Machine Learning (ML) has proven to be a fruitful one, as evidenced by the number of scientific disciplines that have seen advances through their joint application. One of the most powerful demonstrations has been in the area of computational biology, where the addition of ML techniques has helped ameliorate the lack of clear mechanistic models and often poor statistics which has impeded progress in our understanding. I will discuss the development of a hybrid ML/HPC approach to investigate the behavior of an oncogenic protein on cellular membranes, which is one component of the JDACS4C (Joint Design of Advanced Computing Solutions for Cancer) collaboration between the U.S. Department of Energy and the National Cancer Institute.
This work was performed under the auspices of the U.S. Department of Energy (DOE) by Lawrence Livermore National Laboratory (LLNL) under Contract DE-AC52-07NA27344 and under the auspices of the National Cancer Institute (NCI) by Frederick National Laboratory for Cancer Research (FNLCR) under Contract 75N91019D00024. This work has been supported by the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) program established by the U.S. DOE and the NCI of the National Institutes of Health.
Abstract Author(s): Frederick H. Streitz