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

Statistical and Machine Learning Met...

Wang, Jiuzhou,

Linked to FindBook

Google Book

Amazon

博客來

Statistical and Machine Learning Methods for Multi-View Multi-Cohort Biomedical Data Integration /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Statistical and Machine Learning Methods for Multi-View Multi-Cohort Biomedical Data Integration // Jiuzhou Wang.
Author:	Wang, Jiuzhou,
Description:	1 electronic resource (179 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-05, Section: B.
Contained By:	Dissertations Abstracts International86-05B.
Subject:	Biostatistics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31561993
ISBN:	9798346513858

Statistical and Machine Learning Methods for Multi-View Multi-Cohort Biomedical Data Integration /
Wang, Jiuzhou,

Statistical and Machine Learning Methods for Multi-View Multi-Cohort Biomedical Data Integration /Jiuzhou Wang. - 1 electronic resource (179 pages)

Source: Dissertations Abstracts International, Volume: 86-05, Section: B.

The dramatic proliferation of omics data in biomedical research has allowed increasingly comprehensive investigations spanning multiple distinct sample sets (called multi-cohort data, e.g., patients of different cancer types) and multiple molecular facets (called multi-view data, e.g., gene expression, proteomics, and clinical records for the same patients). Statistical approaches that combine multiple datasets are more powerful, efficient, and informative than separate analyses. We develop several novel methods to analyze multi-view multi-cohort data by modeling their complex relationships and uncovering their complex structures. These methods will facilitate more accurate prediction, classification and missing data imputation and address new scientific questions. In the first project, we propose Deep IDA (Integrative Discriminant Analysis), a deep learning method to learn nonlinear projections of two or more views that maximally associate the views and separate the classes in each view. We consider a homogeneous ensemble approach for feature ranking in order to identify variables from each view that contribute most to the association of the views and the separation of the classes within each view, resulting in interpretable findings. Through our framework, we identify signatures that better discriminate COVID-19 patient groups, and relate to neurological conditions, cancer, and metabolic diseases, corroborating current research findings and heightening the need to study the post sequelae effects of COVID-19 to devise effective treatments and to improve patient care.In the second project, to address variation architectures correctly and comprehensively for high-dimensional data across multiple sample sets (i.e., cohorts), we propose multiple augmented reduced rank regression (maRRR), a flexible matrix regression and factorization method to concurrently learn both covariate-driven and auxiliary structured variation. In the third project, we propose the Bi-dimensional Augmented Reduced Rank Regression (baRRR) method, as an extension to maRRR in the second project. This innovative approach is tailored to model mulyi covariate-related and unrelated effects concurrently, capturing diverse effect levels-global, partially-shared, and individual-across both cohorts (i.e. groups) and views (i.e. sources, modalities). We apply maRRR to gene expression data from multiple cancer types (i.e., pan-cancer) from TCGA, with somatic mutations as covariates. Similarly, we apply baRRR to pan-cancer and furthermore pan-omics (mRNA, miRNA, DNA methylation, proteins) data from TCGA. Both methods perform well with respect to prediction and imputation of held-out data, and provide new insights into mutation-driven and auxiliary variation that is shared or specific to certain cancer types and/or molecular modalities.

English

ISBN: 9798346513858Subjects--Topical Terms:

1002712
Biostatistics.
Subjects--Index Terms:

Cancer

Statistical and Machine Learning Methods for Multi-View Multi-Cohort Biomedical Data Integration /
LDR:04334nmm a22004453i 4500 001 2429396
005 20250430100817.5
006 m o d
007 cr|nu||||||||
008 261011s2024 miu||||||m |||||||eng d
020 $a 9798346513858
035 $a (MiAaPQD)AAI31561993
035 $a AAI31561993
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a Wang, Jiuzhou, $e author. $3 3817220
245 1 0 $a Statistical and Machine Learning Methods for Multi-View Multi-Cohort Biomedical Data Integration / $c Jiuzhou Wang.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 1 electronic resource (179 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: 86-05, Section: B.
500 $a Advisors: Lock, Eric F. Committee members: Safo, Sandra E.; Guan, Weihua; Lu, Zhaosong.
502 $b Ph.D. $c University of Minnesota $d 2024.
520 $a The dramatic proliferation of omics data in biomedical research has allowed increasingly comprehensive investigations spanning multiple distinct sample sets (called multi-cohort data, e.g., patients of different cancer types) and multiple molecular facets (called multi-view data, e.g., gene expression, proteomics, and clinical records for the same patients). Statistical approaches that combine multiple datasets are more powerful, efficient, and informative than separate analyses. We develop several novel methods to analyze multi-view multi-cohort data by modeling their complex relationships and uncovering their complex structures. These methods will facilitate more accurate prediction, classification and missing data imputation and address new scientific questions. In the first project, we propose Deep IDA (Integrative Discriminant Analysis), a deep learning method to learn nonlinear projections of two or more views that maximally associate the views and separate the classes in each view. We consider a homogeneous ensemble approach for feature ranking in order to identify variables from each view that contribute most to the association of the views and the separation of the classes within each view, resulting in interpretable findings. Through our framework, we identify signatures that better discriminate COVID-19 patient groups, and relate to neurological conditions, cancer, and metabolic diseases, corroborating current research findings and heightening the need to study the post sequelae effects of COVID-19 to devise effective treatments and to improve patient care.In the second project, to address variation architectures correctly and comprehensively for high-dimensional data across multiple sample sets (i.e., cohorts), we propose multiple augmented reduced rank regression (maRRR), a flexible matrix regression and factorization method to concurrently learn both covariate-driven and auxiliary structured variation. In the third project, we propose the Bi-dimensional Augmented Reduced Rank Regression (baRRR) method, as an extension to maRRR in the second project. This innovative approach is tailored to model mulyi covariate-related and unrelated effects concurrently, capturing diverse effect levels-global, partially-shared, and individual-across both cohorts (i.e. groups) and views (i.e. sources, modalities). We apply maRRR to gene expression data from multiple cancer types (i.e., pan-cancer) from TCGA, with somatic mutations as covariates. Similarly, we apply baRRR to pan-cancer and furthermore pan-omics (mRNA, miRNA, DNA methylation, proteins) data from TCGA. Both methods perform well with respect to prediction and imputation of held-out data, and provide new insights into mutation-driven and auxiliary variation that is shared or specific to certain cancer types and/or molecular modalities.
546 $a English
590 $a School code: 0130
650 4 $a Biostatistics. $3 1002712
650 4 $a Statistics. $3 517247
650 4 $a Bioinformatics. $3 553671
653 $a Cancer
653 $a Data integration
653 $a Deep learning
653 $a Low rank matrix decomposition
653 $a Missing data imputation
653 $a Multi-omics
690 $a 0308
690 $a 0463
690 $a 0715
710 2 $a University of Minnesota. $b Biostatistics. $e degree granting institution. $3 3817221
720 1 $a Lock, Eric F. $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-05B.
790 $a 0130
791 $a Ph.D.
792 $a 2024
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31561993