While our group is always looking for interesting data sets, we have found the machine learning in the biomedical domain to be particularly fruitful. Several paths of inquiry have included developing more advanced network models of brain connectivity, extracting biomolecular interaction knowledge from research texts, and using recurrent neural networks to find meaningful patterns for diagnosis in clinical time series data.
Other links of interest include:
Big Mechanism project description
Cancer in the Time of Algorithms for blog post, news article, and podcast about Greg Ver Steeg and Shirley Pepke’s inspiring work identifying signals in gene expression data for patients with ovarian cancer, a disease Pepke herself is fighting.
Dan presents his work, “A Continuous Model of Cortical Connectivity” , for which he won the Young Scientist award at MICCAI!
The Machine Learning in Health Care symposium, for which Dave Kale is an organizer.