New Machine Learning

Data can be used across domains with limitations removed

Published: Wednesday, April 12, 2017 - 12:00

(Lawrence Berkeley National Lab: Berkeley, CA) -- A team of scientists at Berkeley Lab has developed an unsupervised multiscale machine learning technique that can automatically and specifically capture biomedical events or concepts directly from raw data.

In many data-driven biomedical studies, the data limitations (e.g., limited data scale, limited data label, unbalanced data, and uncontrollable experimental factors) impose great challenges to scientific discovery, which can only be addressed with advanced machine learning techniques. This work is described in the article, “Unsupervised Transfer Learning via Multi-Scale Convolutional Sparse Coding for Biomedical Applications,” published January 2017, in the IEEE Transactions on Pattern Analysis and Machine Intelligence journal. The work provides an effective and efficient way of learning and targeting sharable information so data can be used across domains. It also potentially removes limitations, especially for biomedical studies.

This multiscale machine learning technique can be applied to many biomedical tasks, allowing the efficient and effective capturing of biomedical events or concepts at different scales (e.g., physical size) without any pre-defined biomedical endpoints or studies. An example of a pre-defined endpoint could be differentiation of tumor morphology that can predict metastatic risk. Researchers have shown that the information captured through this technique can be directly deployed or fine-tuned toward new endpoints or studies in related biomedical domains.

The core group in this team, Hang Chang, Antoine M. Snijders, and Jian-Hua Mao, together with another scientist, Zhong Wang, of Berkeley Lab’s Biosciences Area, have initiated a Berkeley Biomedical Data Science Center (BBDS), which combines expertise across multiple disciplines to further facilitate and nurture data-intensive biomedical science.

“We have shown that our technique can be applied to other diverse biomedical tasks,” says Hang Chang, a research scientist in the Lab’s Biological Systems & Engineering Division. “For instance, the knowledge derived from human brain tumor histology can be directly utilized for the differentiation of mouse mammary tumor morphology between radiation-induced cancer and spontaneous cancer. This suggests that our technique can be beneficial to biomedical studies with translational potential.”

The multiscale machine learning technique helps improve the effectiveness and efficiency in learning sharable information across domains.

“When we determine basis information from data collected from cell culture or animal model studies, we think it will be possible to share and deploy the pre-attained information in human-related studies,” adds Chang.

The BBDS plans to apply this technique to three ongoing projects related to cancer risk assessment of environment exposure, early stage cancer diagnosis, and multimodal biomarkers identification for personalized medicine.

For more information, visit the BBDS website.

Berkeley Biomedical Data Science Center. Click here for larger image.

About The Author

Lawrence Berkeley National Laboratory

Founded in 1931, on the belief that the biggest scientific challenges are best addressed by teams, Lawrence Berkeley National Laboratory (Berkeley Lab) and its scientists have been recognized with 13 Nobel Prizes. Today, Berkeley Lab researchers develop sustainable energy and environmental solutions, create useful new materials, advance the frontiers of computing, and probe the mysteries of life, matter, and the universe. Scientists from around the world rely on the Lab’s facilities for their own discovery science. Berkeley Lab is a multiprogram national laboratory managed by the University of California for the U.S. Department of Energy’s Office of Science.