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 到查詢結果 [ null ]

切換: 標籤 | MARC模式 | ISBD

A Data-Driven Modeling Pipeline for ...

Denaro, Christopher,

FindBook

Google Book

Amazon

博客來

A Data-Driven Modeling Pipeline for Quantitative Systems Pharmacology /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	A Data-Driven Modeling Pipeline for Quantitative Systems Pharmacology // Christopher Denaro.
作者:	Denaro, Christopher,
面頁冊數:	1 electronic resource (205 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-11, Section: B.
Contained By:	Dissertations Abstracts International86-11B.
標題:	Bioinformatics. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=32003272
ISBN:	9798314897805

A Data-Driven Modeling Pipeline for Quantitative Systems Pharmacology /
Denaro, Christopher,

A Data-Driven Modeling Pipeline for Quantitative Systems Pharmacology /Christopher Denaro. - 1 electronic resource (205 pages)

Source: Dissertations Abstracts International, Volume: 86-11, Section: B.

Computational methods are becoming commonly used in many areas of medical research. Recently, the field of Quantitative Systems Pharmacology (QSP) emerged to address the need for mathematical models to capture the complex interplay between therapeutic approaches and underlying disease pathophysiology. In a sense, QSP modeling incorporates approaches from physiologically based pharmacokinetics (PBPK), systems biology, and disease modeling, with the explicit goal of model design and output being motivated by quantitative data. In this thesis, we propose a computational framework utilizing a generalization of mathematical graphs called hypergraphs to model metabolic and disease networks; we apply this framework directly to the combination therapy treatment of Tuberculosis, combining heterogeneous data sources of gene expression levels via microarray data and metabolic networks. Next, we demonstrate that machine learning architectures that have classically been applied to natural language problems can be trained to learn network simulation equilibria, providing a proof-of-concept of traditional machine learning approaches to a biological setting. We transition into a literature review that examines the connection between infection with SARS-CoV-2 and neurodegeneration through the lens of mitochondrial function. Next, we motivate and explore applications of the computational framework used in treatment optimization of Tuberculosis to therapeutic biomarker identification in genetic variants of Parkinson's Disease (PD). We also review publicly available data sources for genetic variants of PD and propose a collaborative and integrative computational platform for future model development.

English

ISBN: 9798314897805Subjects--Topical Terms:

553671
Bioinformatics.
Subjects--Index Terms:

Computational biology

A Data-Driven Modeling Pipeline for Quantitative Systems Pharmacology /
LDR:03185nmm a22004213i 4500 001 2433494
005 20250825062343.5
006 m o d
007 cr|nu||||||||
008 261011s2025 miu||||||m |||||||eng d
020 $a 9798314897805
035 $a (MiAaPQD)AAI32003272
035 $a AAI32003272
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a Denaro, Christopher, $e author. $3 3823134
245 1 2 $a A Data-Driven Modeling Pipeline for Quantitative Systems Pharmacology / $c Christopher Denaro.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2025
300 $a 1 electronic resource (205 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 86-11, Section: B.
500 $a Advisors: Piccoli, Benedetto Committee members: Dehzangi, Iman; Barrett, Jeffrey S.; Solesio, Maria E.
502 $b Ph.D. $c Rutgers, The State University of New Jersey - Camden $d 2025.
520 $a Computational methods are becoming commonly used in many areas of medical research. Recently, the field of Quantitative Systems Pharmacology (QSP) emerged to address the need for mathematical models to capture the complex interplay between therapeutic approaches and underlying disease pathophysiology. In a sense, QSP modeling incorporates approaches from physiologically based pharmacokinetics (PBPK), systems biology, and disease modeling, with the explicit goal of model design and output being motivated by quantitative data. In this thesis, we propose a computational framework utilizing a generalization of mathematical graphs called hypergraphs to model metabolic and disease networks; we apply this framework directly to the combination therapy treatment of Tuberculosis, combining heterogeneous data sources of gene expression levels via microarray data and metabolic networks. Next, we demonstrate that machine learning architectures that have classically been applied to natural language problems can be trained to learn network simulation equilibria, providing a proof-of-concept of traditional machine learning approaches to a biological setting. We transition into a literature review that examines the connection between infection with SARS-CoV-2 and neurodegeneration through the lens of mitochondrial function. Next, we motivate and explore applications of the computational framework used in treatment optimization of Tuberculosis to therapeutic biomarker identification in genetic variants of Parkinson's Disease (PD). We also review publicly available data sources for genetic variants of PD and propose a collaborative and integrative computational platform for future model development.
546 $a English
590 $a School code: 1652
650 4 $a Bioinformatics. $3 553671
650 4 $a Systematic biology. $3 3173492
650 4 $a Applied mathematics. $3 2122814
653 $a Computational biology
653 $a Mathematical modeling
653 $a Quantitative systems pharmacology
653 $a Systems biology
690 $a 0715
690 $a 0423
690 $a 0364
710 2 $a Rutgers, The State University of New Jersey - Camden. $b Computational and Integrative Biology. $e degree granting institution. $3 3823135
720 1 $a Piccoli, Benedetto $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-11B.
790 $a 1652
791 $a Ph.D.
792 $a 2025
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=32003272