New machine learning technique provides translational results
A team of scientists at Berkeley Lab has developed an unsupervised multi-scale 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 un-controllable experimental factors) impose great challenges to scientific discovery, which can only be addressed with advanced machine learning techniques. This work, described recently in IEEE Transactions on Pattern Analysis and Machine Intelligence, 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.