Scalable sketching and indexing algorithms for large biological datasets
DNA sequencing data continues to progress towards longer sequencing reads with increasingly lower error rates. In order to efficiently process the ever-growing collections of sequencing data, there is a crucial need for more time- and memory-efficient algorithms and data structures. In this thesis,...
Main Author: | Ekim, Bariş C. |
---|---|
Other Authors: | Berger, Bonnie A. |
Format: | Thesis |
Published: |
Massachusetts Institute of Technology
2023
|
Online Access: | https://hdl.handle.net/1721.1/147392 |
Similar Items
-
Biologically-Plausible Learning Algorithms Can Scale to Large Datasets
by: Xiao, Will, et al.
Published: (2018) -
Biologically-plausible learning algorithms can scale to large datasets
by: Xiao, Will, et al.
Published: (2019) -
A Randomized Parallel Algorithm for Efficiently Finding Near-Optimal Universal Hitting Sets
by: Ekim, Barış, et al.
Published: (2022) -
Scalable Metropolis-Hastings for exact Bayesian inference with large datasets
by: Cornish, R, et al.
Published: (2019) -
Hopper: a mathematically optimal algorithm for sketching biological data
by: DeMeo, Benjamin, et al.
Published: (2022)