東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Nonparametric Methods for Building a...

Amin, Alan N.

FindBook

Google Book

Amazon

博客來

Nonparametric Methods for Building and Evaluating Models of Biological Sequences.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Nonparametric Methods for Building and Evaluating Models of Biological Sequences./
作者:	Amin, Alan N.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:	417 p.
附註:	Source: Dissertations Abstracts International, Volume: 85-05, Section: B.
Contained By:	Dissertations Abstracts International85-05B.
標題:	Systematic biology. -
電子資源:	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.
LDR:02153nmm a2200397 4500 001 2399403
005 20240916065429.5
006 m o d
007 cr#unu||||||||
008 251215s2023 ||||||||||||||||| ||eng d
020 $a 9798380848091
035 $a (MiAaPQ)AAI30573022
035 $a AAI30573022
040 $a MiAaPQ $c MiAaPQ
100 1 $a Amin, Alan N. $0 (orcid)0000-0002-2656-8273 $3 3769370
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.
650 4 $a Systematic biology. $3 3173492
650 4 $a Statistics. $3 517247
650 4 $a Bioinformatics. $3 553671
650 4 $a Pharmaceutical sciences. $3 3173021
653 $a Probabilistic models
653 $a Drugs
653 $a Biological sequences
653 $a Assumptions
653 $a Sequence data
690 $a 0423
690 $a 0463
690 $a 0715
690 $a 0572
710 2 $a Harvard University. $b Systems Biology. $3 2096528
773 0 $t Dissertations Abstracts International $g 85-05B.
790 $a 0084
791 $a Ph.D.
792 $a 2023
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30573022