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Nonparametric Methods for Building a...

Amin, Alan N.

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Nonparametric Methods for Building and Evaluating Models of Biological Sequences.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Nonparametric Methods for Building and Evaluating Models of Biological Sequences./
Author:	Amin, Alan N.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	417 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-05, Section: B.
Contained By:	Dissertations Abstracts International85-05B.
Subject:	Systematic biology. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30573022
ISBN:	9798380848091

Nonparametric Methods for Building and Evaluating Models of Biological Sequences.
Amin, Alan N.

Nonparametric Methods for Building and Evaluating Models of Biological Sequences. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 417 p.

Source: Dissertations Abstracts International, Volume: 85-05, Section: B.

Thesis (Ph.D.)--Harvard University, 2023.

Probabilistic models of biological sequences are used to design drugs, make predictions about human health, and learn basic biology. Sequence data is high dimensional so a probabilistic model must make biological assumptions to predict and infer. However, these assumptions can come at the cost of the flexibility of the model, fundamentally limiting its ability to make accurate predictions and learn new biology. Modern sequencing efforts and high-throughput experimentation are generating an ever-increasing amount of sequence data, in principle providing increasing information to learn the complexity of real sequence data. To leverage this wealth of data this thesis builds nonparametric models and tests of sequences that incorporate biological prior knowledge while remaining flexible. This thesis build methods to perform efficient, flexible, and reliable prediction and inference from DNA and protein data, at large and small scale, and in supervised and unsupervised settings.

ISBN: 9798380848091Subjects--Topical Terms:

3173492
Systematic biology.
Subjects--Index Terms:

Probabilistic models

Nonparametric Methods for Building and Evaluating Models of Biological Sequences.
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245 1 0 $a Nonparametric Methods for Building and Evaluating Models of Biological Sequences.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 417 p.
500 $a Source: Dissertations Abstracts International, Volume: 85-05, Section: B.
500 $a Advisor: Marks, Debora Susan.
502 $a Thesis (Ph.D.)--Harvard University, 2023.
520 $a Probabilistic models of biological sequences are used to design drugs, make predictions about human health, and learn basic biology. Sequence data is high dimensional so a probabilistic model must make biological assumptions to predict and infer. However, these assumptions can come at the cost of the flexibility of the model, fundamentally limiting its ability to make accurate predictions and learn new biology. Modern sequencing efforts and high-throughput experimentation are generating an ever-increasing amount of sequence data, in principle providing increasing information to learn the complexity of real sequence data. To leverage this wealth of data this thesis builds nonparametric models and tests of sequences that incorporate biological prior knowledge while remaining flexible. This thesis build methods to perform efficient, flexible, and reliable prediction and inference from DNA and protein data, at large and small scale, and in supervised and unsupervised settings.
590 $a School code: 0084.
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650 4 $a Statistics. $3 517247
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650 4 $a Pharmaceutical sciences. $3 3173021
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653 $a Biological sequences
653 $a Assumptions
653 $a Sequence data
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773 0 $t Dissertations Abstracts International $g 85-05B.
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