Cell Cycle

Conserved cell cycle genes in four species

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

  • 11/2013

    SDREM selected as a top systems biology paper. The RECOMB/ISCB Conference on Regulatory and Systems Genomics, selected the Top Ten Papers in the field of Regulatory and Systems Genomics for 2013. We were happy to see that our SDREM paper, that appeared in 2013 in Genome Research is part of that list. Congratulations to Tony!

  • 10/2013

    ExpressionBlast mines large expression datasets. We report in a new paper in Nature Methods on a new web based tool, ExpressionBlast, that can be used to compare expression experiments, within and across species, to hundreds of thousands of arrays deposited in large databases. The tool provides an easy to use GUI and has already been used to successfully study improtant mammalian genes. ExpressionBlast is available online with documentation and support. See the CMU press release and CMU's front page coverage. The method was also highlighted by a number of news outlets including Bio-IT World.

  • 09/2013

    mirDREM reconstructs development and disease networks. A new paper by Lane fellow Marcel Schulz was just published in the Proceedings of the National Academy of Science (PNAS). The paper introduces a new method for reconstructing dynamic regulatory networks that are co-regulated by transcription factors and microRNAs. The paper was featured on the cover of PNAS. In addition, PNAS published a very nice commentary explaining the importance of the method and application. The paper has also been highlighted in a number of news outlets including Nature's SciBX and GenomeWeb . The method can be downloaded from the supporting website.

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