Regulatory Networks

Application of the Dynamic Regulatory Events Miner (DREM) to study amino acid starvation response in yeast.

Research

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.

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Latest Publications

Full list of publications

News

  • 11/2015

    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.

  • 07/2015

    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 )

  • 07/2015

    Ercument Cicek accepts new faculty position. Ercument, who is a postdoc in our group since 2013 (also a Lane fellow co-advised by Kathryn Roeder) has accepted a faculty position in the Computer Science department at Bilkent University, one of the leading research universities in Turkey. We wish him good luck on this exciting new appointment!

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