@article{abeysinghe2008graphmatchJournal,
 author = {Sasakthi Abeysinghe and Tao Ju and Matthew L. Baker and Wah Chiu},
 title = {Shape modeling and matching in identifying 3D protein structures},
 journal = {Computer-Aided Design},
 volume = {40},
 number = {6},
 pages = {708--720},
 year = {2008},
 month = {June},
 doi = {doi:10.1016/j.cad.2008.01.013},
 publisher={Elsevier},
 abstract = {In this paper, we describe a novel geometric approach in the
    process of recovering 3D protein structures from scalar
    volumes. The input to our method is a sequence of
    alpha-helices that make up a protein, and a low-resolution
    protein density volume where possible locations of
    alpha-helices have been detected. Our task is to identify
    the correspondence between the two sets of helices, which
    will shed light on how the protein folds in space. The
    central theme of our approach is to cast the correspondence
    problem as that of shape matching between the 3D volume and
    the 1D sequence. We model both shapes as attributed
    relational graphs, and formulate a constrained inexact graph
    matching problem. To compute the matching, we developed an
    optimal algorithm based on the A*-search with several choices
    of heuristic functions. As demonstrated in a suite of
    synthetic and authentic inputs, the shape-modeling approach
    is capable of identifying helix correspondences in
    noise-abundant volumes at high accuracy with minimal or no
    user intervention.},
}



@inproceedings{abeysinghe2007graphmatchPaper,
 author = {Sasakthi S. Abeysinghe and Tao Ju and Wah Chiu and Matthew Baker},
 title = {Shape modeling and matching in identifying protein structure from low-resolution images},
 booktitle = {SPM '07: Proceedings of the 2007 ACM symposium on Solid and physical modeling},
 year = {2007},
 isbn = {978-1-59593-666-0},
 pages = {223--232},
 location = {Beijing, China},
 doi = {http://doi.acm.org/10.1145/1236246.1236278},
 publisher = {ACM},
 address = {New York, NY, USA},
 abstract = {In this paper, we describe a novel, shape-modeling approach to recovering 3D protein
 	structures from volumetric images. The input to our method is a sequence of a-helices that make up
 	a protein, and a low-resolution volumetric image of the protein where possible locations of alpha-helices
 	have been detected. Our task is to identify the correspondence between the two sets of helices, which
 	will shed light on how the protein folds in space. The central theme of our approach is to cast the
 	correspondence problem as that of shape matching between the 3D volume and the 1D sequence. We model
 	both the shapes as attributed relational graphs, and formulate a constrained inexact graph matching problem.
 	To compute the matching, we developed an optimal algorithm based on the A*-search with several choices of
 	heuristic functions. As demonstrated in a suite of real protein data, the shape-modeling approach is capable
 	of correctly identifying helix correspondences in noise-abundant volumes with minimal or no user intervention.},
}



@mastersthesis{abeysinghe2007graphmatchThesis,
 author = {Sasakthi S. Abeysinghe and Tao Ju},
 title = {Determining a-helix correspondence for protein structure prediction from cryo-EM density maps},
 school = {Washington University}
 address = {St. Louis, MO, USA},
 month = {May}
 year = {2007},
 abstract = {Determining protein structure is an important problem for structural biologists, which has
		received a significant amount of attention in the recent years. In this thesis, we describe
		a novel, shape-modeling approach as an intermediate step towards recovering 3D protein
		structures from volumetric images. The input to our method is a sequence of a-helices that
		make up a protein, and a low-resolution volumetric image of the protein where possible
		locations of a-helices have been detected. Our task is to identify the correspondence
		between the two sets of helices, which will shed light on how the protein folds in space.
		The central theme of our approach is to cast the correspondence problem as that of shape
		matching between the 3D volume and the 1D sequence. We model both the shapes as
		attributed relational graphs, and formulate a constrained inexact graph matching problem.
		To compute the matching, we developed an optimal algorithm based on the A*-search with
		several choices of heuristic functions. As demonstrated in a suite of real protein data,
		the shape-modeling approach is capable of correctly identifying helix correspondences in
		noise-abundant volumes with minimal or no user intervention.},
}