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Biomedical Engineering and Bioengineering Commons™
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Full-Text Articles in Biomedical Engineering and Bioengineering
Extracting The Structure And Conformations Of Biological Entities From Large Datasets, Ali Dashti
Extracting The Structure And Conformations Of Biological Entities From Large Datasets, Ali Dashti
Theses and Dissertations
In biology, structure determines function, which often proceeds via changes in conformation. Efficient means for determining structure exist, but mapping conformations continue to present a serious challenge. Single-particles approaches, such as cryogenic electron microscopy (cryo-EM) and emerging "diffract & destroy" X-ray techniques are, in principle, ideally positioned to overcome these challenges. But the algorithmic ability to extract information from large heterogeneous datasets consisting of "unsorted" snapshots - each emanating from an unknown orientation of an object in an unknown conformation - remains elusive.
It is the objective of this thesis to describe and validate a powerful suite of manifold-based algorithms …
Efficient Computation Of K-Nearest Neighbor Graphs For Large High-Dimensional Data Sets On Gpu Clusters, Ali Dashti
Efficient Computation Of K-Nearest Neighbor Graphs For Large High-Dimensional Data Sets On Gpu Clusters, Ali Dashti
Theses and Dissertations
The k-Nearest Neighbor Graph (k-NNG) and the related k-Nearest Neighbor (k-NN) methods have a wide variety of applications in areas such as bioinformatics, machine learning, data mining, clustering analysis, and pattern recognition. Our application of interest is manifold embedding. Due to the large dimensionality of the input data (<15k), spatial subdivision based techniques such OBBs, k-d tree, BSP etc., are not viable. The only alternative is the brute-force search, which has two distinct parts. The first finds distances between individual vectors in the corpus based on a pre-defined metric. Given the distance matrix, the second step selects k nearest neighbors for each member of the query data set.
This thesis presents the development and implementation of a distributed exact k-Nearest Neighbor Graph (k-NNG) construction method. The proposed method uses Graphics Processing Units (GPUs) and exploits multiple levels of parallelism for distributed computational systems using GPUs. It is scalable for different cluster sizes, with each compute node in the cluster …