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Picture Aaron Lindsey
Undergraduate
Algorithms & Applications Group

Parasol Laboratory url: http://parasol.tamu.edu/~alindsey/
Department of Computer Science and Engineering email:
Texas A&M University office: 407 HRBB
College Station, TX 77843-3112 tel:
USA fax: (979) 458-0718


Howdy! My name is Aaron Lindsey and I am an undergraduate student at Texas A&M University where I study Computer Science. My research focuses on computational biology and specifically algorithms for protein folding simulations.


Research Projects

Improving Decoy Sets for Protein Folding Simulations

This research involves evaluating the quality of and improving protein decoy sets. A decoy is a computer-generated protein structure that is similar to the native state of a protein but is not biologically real. These structures are used to test the ability of a protein folding algorithm to choose the protein's native state among incorrect protein conformations. Better decoy sets allow scientists and engineers to create more accurate protein folding simulations, leading to new treatments for diseases such as Alzheimer's and Mad Cow disease which are commonly associated with protein mis-folding. We present a method to evaluate and improve the quality of decoy databases by adding novel structures and/or removing redundant structures. We test our approach on 17 different decoy databases of varying size and type and show significant improvement across a variety of metrics.

My faculty mentor for this project is Dr. Nancy Amato and my graduate student mentor is Cindy (Hsin-Yi) Yeh.

I participated in the following research programs while working on this project:

For more information about this project, see our project page and TR13-011.

Uniformly Sampling the Medial Axis

Motion planning algorithms that utilize the medial axis, or the set of all points equidistance to two or more obstacles, are very useful because they tend to generate high clearance paths which minimize the risk of a robot colliding with an obstacle along the path. However, they are biased heavily to toward certain portions of the medial axis which makes solving certain problems very difficult, e.g., specific narrow passages. We introduce Uniform Medial-Axis Probabilistic RoadMap (UMAPRM), a novel planning variant that generates samples uniformly on the medial axis of the free portion of C-space. Our results show that UMAPRM's distribution of samples along the medial axis is favorable to other medial axis samplers in certain planning problems.

For more information about this project, see our paper.


Publications

Hsin-Yi (Cindy) Yeh, Aaron Lindsey, Chih-Peng Wu, Shawna Thomas, Nancy M. Amato, "Decoy Database Improvement for Protein Folding," Journal of Computational Biology, 22(9):823 - 836, Sep 2015.
Journal(pdf, abstract)

Aaron Lindsey, Hsin-Yi (Cindy) Yeh, Chih-Peng Wu, Shawna Thomas, Nancy M. Amato, "Improving Decoy Databases for Protein Folding Algorithms," In ACM Conf. on Bioinformatics, Comput. Biology and Health Informatics on Computational Structural Bioinformatics Wkshp., pp. 717 - 724, Newport Beach, CA, Sep 2014.
Proceedings(ps, pdf, abstract)

Aaron Lindsey, Hsin-Yi (Cindy) Yeh, Chih-Peng Wu, Shawna Thomas, Nancy M. Amato, "Improving Decoy Databases for Protein Folding Algorithms ," In Proc.of 2014 RSS Wkshp. on Robotics Methods for Structural and Dynamic Modeling of Molecular Systems, Berkeley, CA, Jul 2014.
Proceedings(ps, pdf, abstract)

Hsin-Yi (Cindy) Yeh, Jory Denny, Aaron Lindsey, Shawna L. Thomas, Nancy M. Amato, "UMAPRM: Uniformly Sampling the Medial Axis," In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 5798 - 5803, Hong Kong, China, Jun 2014.
Proceedings(ps, pdf, abstract)


Last Updated: Nov. 1, 2014