東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

The Development and Application of M...

Stover, Danielle Michelle-Maeser,

Linked to FindBook

Google Book

Amazon

博客來

The Development and Application of Machine Learning for Drug Discovery and Drug Response Prediction for Personalized Cancer Treatment /

Record Type:	Electronic resources : Monograph/item
Title/Author:	The Development and Application of Machine Learning for Drug Discovery and Drug Response Prediction for Personalized Cancer Treatment // Danielle Michelle-Maeser Stover.
Author:	Stover, Danielle Michelle-Maeser,
Description:	1 electronic resource (158 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-09, Section: B.
Contained By:	Dissertations Abstracts International85-09B.
Subject:	Bioinformatics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30991067
ISBN:	9798381952537

The Development and Application of Machine Learning for Drug Discovery and Drug Response Prediction for Personalized Cancer Treatment /
Stover, Danielle Michelle-Maeser,

The Development and Application of Machine Learning for Drug Discovery and Drug Response Prediction for Personalized Cancer Treatment /Danielle Michelle-Maeser Stover. - 1 electronic resource (158 pages)

Source: Dissertations Abstracts International, Volume: 85-09, Section: B.

In the field of pharmacogenomics and precision medicine, gene expression analysis has become a crucial tool in predicting patient drug response. My contributions to this field come primarily in the development and application of two bioinformatic packages: oncoPredict and scIDUC. oncoPredict is a tool based in the R programming language, primarily used to predict the response of various cancer samples (cell line, patient, etc.) to different drugs. This is made possible by incorporating machine learning to analyze the complex relationships between genomic features and drug response from pan-cancer cell lines. These relationships are learned from microarray or bulk RNA sequencing (RNA-seq) data and high-throughput drug screens, then applied to patient data to generate novel drug discovery hypotheses. In turn, oncoPredict aids in identifying potential drug candidates, understanding mechanisms of drug resistance, and predicting the effectiveness of drugs on specific cancer types. scIDUC (single-cell Integration and Drug Utility Computation) is a computational framework based in python that quickly and accurately generates predictions of drug response for cells derived from single-cell RNA sequencing (scRNA-seq) data. It is a transfer learning-based approach that learns relationships between drug sensitivities and relevant gene expression patterns based on cell line bulk RNA-seq data and high-throughput drug screens, similar to oncoPredict. The key difference, however, is that prior to training drug response models, scIDUC integrates bulk RNA-seq and target scRNA-seq data to denoise and extract shared gene expression patterns between bulk and single-cell data sources. The resulting bulk data is then used to train drug response models, whose coefficients are further applied to post-integration single-cell data to infer cellular drug sensitivity scores.

English

ISBN: 9798381952537Subjects--Topical Terms:

553671
Bioinformatics.
Subjects--Index Terms:

Cancer research

The Development and Application of Machine Learning for Drug Discovery and Drug Response Prediction for Personalized Cancer Treatment /
LDR:03536nmm a22004813i 4500 001 2437476
005 20251124054751.5
006 m o d
007 cr|nu||||||||
008 261011s2024 miu||||||m |||||||eng d
020 $a 9798381952537
035 $a (MiAaPQD)AAI30991067
035 $a AAI30991067
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a Stover, Danielle Michelle-Maeser, $e author. $3 3828462
245 1 0 $a The Development and Application of Machine Learning for Drug Discovery and Drug Response Prediction for Personalized Cancer Treatment / $c Danielle Michelle-Maeser Stover.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 1 electronic resource (158 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: 85-09, Section: B.
500 $a Advisors: Myers, Chad; Huang, R. Stephanie Committee members: Largaespada, David; Olin, Michael.
502 $b Ph.D. $c University of Minnesota $d 2024.
520 $a In the field of pharmacogenomics and precision medicine, gene expression analysis has become a crucial tool in predicting patient drug response. My contributions to this field come primarily in the development and application of two bioinformatic packages: oncoPredict and scIDUC. oncoPredict is a tool based in the R programming language, primarily used to predict the response of various cancer samples (cell line, patient, etc.) to different drugs. This is made possible by incorporating machine learning to analyze the complex relationships between genomic features and drug response from pan-cancer cell lines. These relationships are learned from microarray or bulk RNA sequencing (RNA-seq) data and high-throughput drug screens, then applied to patient data to generate novel drug discovery hypotheses. In turn, oncoPredict aids in identifying potential drug candidates, understanding mechanisms of drug resistance, and predicting the effectiveness of drugs on specific cancer types. scIDUC (single-cell Integration and Drug Utility Computation) is a computational framework based in python that quickly and accurately generates predictions of drug response for cells derived from single-cell RNA sequencing (scRNA-seq) data. It is a transfer learning-based approach that learns relationships between drug sensitivities and relevant gene expression patterns based on cell line bulk RNA-seq data and high-throughput drug screens, similar to oncoPredict. The key difference, however, is that prior to training drug response models, scIDUC integrates bulk RNA-seq and target scRNA-seq data to denoise and extract shared gene expression patterns between bulk and single-cell data sources. The resulting bulk data is then used to train drug response models, whose coefficients are further applied to post-integration single-cell data to infer cellular drug sensitivity scores.
546 $a English
590 $a School code: 0130
650 4 $a Bioinformatics. $3 553671
650 4 $a Computational chemistry. $3 3350019
650 4 $a Pharmacology. $3 634543
650 4 $a Medicine. $3 641104
650 4 $a Pharmaceutical sciences. $3 3173021
653 $a Cancer research
653 $a Drug discovery
653 $a Drug response prediction
653 $a Machine learning
653 $a Pharmacogenomics
653 $a Precision medicine
690 $a 0715
690 $a 0219
690 $a 0564
690 $a 0419
690 $a 0572
710 2 $a University of Minnesota. $b Biomedical Informatics and Computational Biology. $e degree granting institution. $3 3817045
720 1 $a Myers, Chad $e degree supervisor.
720 1 $a Huang, R. Stephanie $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 85-09B.
790 $a 0130
791 $a Ph.D.
792 $a 2024
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30991067