Carnegie Mellon University
April 18, 2017

Another impressive performance by CBD in ISMB and RECOMB acceptances

Proceedings papers acceptances recently came out for the top two computational biology conferences, Intelligent Systems for Molecular Biology (ISMB) and Research in Computational Molecular Biology (RECOMB).

ISMB 2017

We are very pleased that 5 papers by members of the Computational Biology Department (CBD) have been accepted for presentation at ISMB 2017 – only 16% of submitted papers were accepted.  Carnegie Mellon has been among the leaders in accepted papers at ISMB for many years.  Over 10% of this year’s papers were from CBD authors!  Congratulations to all of our colleagues, but especially to Ph.D. students Yang Yang and Xiongtao Ruan on their papers!

  • Guillaume Marçais, David Pellow, Daniel Bork, Yaron Orenstein, Ron Shamir, Carl Kingsford. Improving the Performance of Minimizers and Winnowing Schemes.
  • Min Xu, Xiaoqi Chai, Hariank Muthankana, Xiaodan Liang, Ge Yang, Tzviya Zeev-Ben-Mordehai, Eric Xing. Deep Learning Based Subdivision Approach for Large Scale Macromolecules Structure Recovery from Electron Cryo Tomograms.
  • Mingfu Shao, Jianzhu Ma, Sheng Wang. DeepBound:  Accurate Identification of Transcript Boundaries via Deep Convolutional Neural Fields.
  • Yang Yang, Ruochi Zhang, Shashank Singh, Jian Ma. Exploiting Sequence-Based Features for Predicting Enhancer-Promoter Interactions.
  • Xiongtao Ruan, Christoph Wülfing, Robert F. Murphy. Image-based Spatiotemporal Causality Inference for Protein Signaling Networks.


Repeating our 2016 performance, CBD once again leads in accepted proceedings papers for the RECOMB conference (see  Congratulations to all, especially to Brad Solomon for his first-author conference paper!

  • Brad Solomon and Carl Kingsford. Improved Search of Large Transcriptomic Sequencing Databases Using Split Sequence Bloom Trees.
  • Dan DeBlasio and John Kececioglu. Boosting Alignment Accuracy by Adaptive Local Realignment.
  • Ashok Rajaraman and Jian Ma. Towards Recovering Allele-specific Cancer Genome Graphs.