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A machine learning approach for desi...

Lim, Darren Troy.

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A machine learning approach for designing DNA sequence assembly algorithms.

Record Type:	Electronic resources : Monograph/item
Title/Author:	A machine learning approach for designing DNA sequence assembly algorithms./
Author:	Lim, Darren Troy.
Description:	95 p.
Notes:	Source: Dissertation Abstracts International, Volume: 64-04, Section: B, page: 1799.
Contained By:	Dissertation Abstracts International64-04B.
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3088511

A machine learning approach for designing DNA sequence assembly algorithms.
Lim, Darren Troy.

A machine learning approach for designing DNA sequence assembly algorithms. - 95 p.

Source: Dissertation Abstracts International, Volume: 64-04, Section: B, page: 1799.

Thesis (Ph.D.)--Rensselaer Polytechnic Institute, 2003.

We present two separate algorithms for solving the DNA sequence assembly problem. The sequence assembly problem is the reconstruction of a large sequence of DNA from a set of subsequences called fragments. Fragments are created by breaking, at random intervals, copies of the original DNA sequence. This creates a system of fragments in which many of the fragments overlap with each other. Identifying these overlapping fragments is the key to reforming the original strand.Subjects--Topical Terms:

626642
Computer Science.

A machine learning approach for designing DNA sequence assembly algorithms.
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035 $a (UnM)AAI3088511
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100 1 $a Lim, Darren Troy. $3 1940773
245 1 0 $a A machine learning approach for designing DNA sequence assembly algorithms.
300 $a 95 p.
500 $a Source: Dissertation Abstracts International, Volume: 64-04, Section: B, page: 1799.
500 $a Adviser: Mark Goldberg.
502 $a Thesis (Ph.D.)--Rensselaer Polytechnic Institute, 2003.
520 $a We present two separate algorithms for solving the DNA sequence assembly problem. The sequence assembly problem is the reconstruction of a large sequence of DNA from a set of subsequences called fragments. Fragments are created by breaking, at random intervals, copies of the original DNA sequence. This creates a system of fragments in which many of the fragments overlap with each other. Identifying these overlapping fragments is the key to reforming the original strand.
520 $a The first algorithm first identifies a “correct” series of fragment merges which would result in producing the original sample from which they were obtained. It enters each series into a database of solutions, which is then used to sequence DNA different than those used to create the database.
520 $a The second algorithm uses a <italic>k</italic>-mer based approach to identifying overlapping regions in fragments. The method is an improvement over the first algorithm in two ways: (1) it is designed to sequence real fragments, which are different in composition from simulated fragments; (2) it can be used to sequence much longer strands of DNA.
520 $a For both algorithms, parameters of computation are learned through experimentation with sequences of previously assembled DNA. Our experiments show that the parameters of computation generated by learning on a set of DNAs can be used to successfully sequence a separate set of DNA sequences.
590 $a School code: 0185.
650 4 $a Computer Science. $3 626642
650 4 $a Biology, Genetics. $3 1017730
690 $a 0984
690 $a 0369
710 2 0 $a Rensselaer Polytechnic Institute. $3 1019062
773 0 $t Dissertation Abstracts International $g 64-04B.
790 1 0 $a Goldberg, Mark, $e advisor
790 $a 0185
791 $a Ph.D.
792 $a 2003
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3088511