Carnegie Mellon University
April 11, 2018

CBD excels again in accepted ISMB papers

We are very pleased that six papers by members of the Computational Biology Department (CBD) have been accepted for presentation at ISMB 2018, one of the most selective computational biology conferences.  ISMB 2018 accepted 65 papers out of a total of 331 submissions.  This year’s results continue Carnegie Mellon’s leadership in accepted papers at ISMB that has been ongoing for many years.

The first authors of two of the papers are first-year students in our Computational Biology Ph.D. program and the first author of a third is a recent graduate of our M.S. in Computational Biology program.

An integration of fast alignment and maximum-likelihood methods for electron subtomogram averaging and classification

Yixiu Zhao, Carnegie Mellon University, United States

Xiangrui Zeng, Carnegie Mellon University, United States

Qiang Guo, Max Planck Institute for Biochemistry, Germany

Min Xu, Carnegie Mellon University, United States

 

Asymptotically optimal minimizers schemes

Guillaume Marcais, Carnegie Mellon University, United States

Dan DeBlasio, Carnegie Mellon University, United States

Carl Kingsford, Carnegie Mellon University, United States

 

Deconvolution and phylogeny inference of structural variations in tumor genomic samples

Jesse Eaton, Carnegie Mellon University, United States

Jingyi Wang, Carnegie Mellon University, United States

Russell Schwartz, Carnegie Mellon University, United States

 

Personalized Regression Enables Sample-Specific Pan-Cancer Analysis

Ben Lengerich, Carnegie Mellon University, United States

Bryon Aragam, Carnegie Mellon University, United States

Eric Xing, Carnegie Mellon University, United States

 

Predicting CTCF-mediated chromatin loops using CTCF-MP

Ruochi Zhang, Carnegie Mellon University, United States

Yuchuan Wang, Carnegie Mellon University, United States

Yang Yang, Carnegie Mellon University, United States

Yang Zhang, Carnegie Mellon University, United States

Jian Ma, Carnegie Mellon University, United States

 

Quantifying the similarity of topological domains across normal and cancer human cell types

Natalie Sauerwald, Carnegie Mellon University, United States

Carl Kingsford, Carnegie Mellon University, United States