Our group develops computational methods for understanding the dynamics, interactions and conservation of complex biological systems. As new high-throughput biological data sources become available, they hold the promise of revolutionizing molecular biology by providing a large-scale view of cellular activity. However, each type of data is noisy, contains many missing values and only measures a single aspect of cellular activity. Our computational focus is on methods for large scale data integration. We primarily rely on machine learning and statistical methods. Most of our work is carried out in close collaboration with experimentalists. Many of the computational tools we develop are available and widely used.
Best paper award at RECOMB 2016! Our paper 'Shall we dense? Comparing design strategies for time series expression experiments' has won the 'Best Paper Award' in this years '20th International Conference on Research in Computational Molecular Biology (RECOMB)'. RECOMB is one of the two most important conferences in the general area of computational biology. The journal version of the paper has been published in the Journal Cell Systems, accompanied by a nice commentary which discusses the work. Congratulations to Emre and Michael.
We received a large grant from the PA Department of Health to improve the usage of 'Big Data' for cancer . The grant will establish a new center, Big Data For Better Health (BD4BH) which will be co-directed by Bar-Joseph and Greg Cooper from the University of Pittsburgh. The main focus would be on developing better methods for integrating, analyzing and modeling large volumes of diverse data on cancer patients. The goal is to produce more accurate predictions of patient outcomes and to enable clinicians to tailor care for each patient. See the press release from CMU for more details.
Neuroscience-based algorithms make for better networks. A new paper we published in PLoS Computational Biology shows that the methods used by the brain to optimize the topology of neural networks (termed pruning) lead to effective and robust computational networks as well. As part of this work, we have experimentally characterized the pruning rates in developing mice and showed that using similar rates to construct computer communication networks improves their ability to efficiently route messages. This paper further expands the set of Algorithms in Nature we have been working on for the last 5 years. The work has been highlighted by a CMU press release , a press release from the Salk Institute, and a few other venues (for example, Tech Times )