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

FindBook

Google Book

Amazon

博客來

Analyzing the Multidimensionality of Aging by Using Machine Learning to Predict Age from Diverse Medical Datasets.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Analyzing the Multidimensionality of Aging by Using Machine Learning to Predict Age from Diverse Medical Datasets./
作者:	Le Goallec, Alan.
面頁冊數:	1 online resource (457 pages)
附註:	Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
Contained By:	Dissertations Abstracts International83-02B.
標題:	Bioinformatics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28498454click for full text (PQDT)
ISBN:	9798534681314

Analyzing the Multidimensionality of Aging by Using Machine Learning to Predict Age from Diverse Medical Datasets.
Le Goallec, Alan.

Analyzing the Multidimensionality of Aging by Using Machine Learning to Predict Age from Diverse Medical Datasets. - 1 online resource (457 pages)

Source: Dissertations Abstracts International, Volume: 83-02, Section: B.

Thesis (Ph.D.)--Harvard University, 2021.

Includes bibliographical references

The world population is aging, leading to a rise in the prevalence of age-related diseases such as cardiovascular disease and cancer. In parallel to treating the diseases, an attractive idea is to address the problem at its root by slowing aging. In contrast to a person's chronological age, which solely measures the time since their birth, biological age represents the state of the person's body, is the true underlying cause of age-related diseases and could potentially be reduced by rejuvenating therapies. Training machine learning algorithms to predict chronological age from biomedical datasets yields biological age predictors. Such predictors, such as the DNA methylation aging clock, can be used to further our understanding of aging as a biological process, to identify new mechanisms or lifestyle changes susceptible to slow down aging, and to assess the efficiency of upcoming rejuvenating therapies. A large number of biological age predictors have already been built on various biomedical datasets such as brain MRI images, full body X-rays and blood samples, but it is currently largely unknown whether these predictors all capture a single, central biological aging process, or if each predictor captures a particular dimension of the complex and multi-faceted process that is aging. In other words, can a forty-year-old participant have the arteries of a fifty-year-old person, and the musculoskeletal health of a thirty-year-old person? In the following, we studied how the correlation structure between blood and anthropometric biomarkers changes with age (Chapter 1), and we predicted infant's age from their gut microbiome with a R2 of 62.5±2.1% (Chapter 2). Finally, (Chapter 3), we leveraged 676,787 samples from 502,211 UK Biobank participants to build 331 age predictors on scalar biomarkers (e.g. blood samples), time series (e.g. electrocardiograms), images (e.g. full body X-rays) and videos (heart MRI). We conclude that, although the different aging dimensions are similarly associated with environmental and socioeconomic variables, they are both phenotypically and genetically largely uncorrelated with each other (respective average correlations of .139±.090 and .104±.149), highlighting the multidimensionality of the aging process.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798534681314Subjects--Topical Terms:

553671
Bioinformatics.
Subjects--Index Terms:

AgingIndex Terms--Genre/Form:

542853
Electronic books.

Analyzing the Multidimensionality of Aging by Using Machine Learning to Predict Age from Diverse Medical Datasets.
LDR:03753nmm a2200421K 4500 001 2357207
005 20230622065016.5
006 m o d
007 cr mn ---uuuuu
008 241011s2021 xx obm 000 0 eng d
020 $a 9798534681314
035 $a (MiAaPQ)AAI28498454
035 $a AAI28498454
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Le Goallec, Alan. $3 3697737
245 1 0 $a Analyzing the Multidimensionality of Aging by Using Machine Learning to Predict Age from Diverse Medical Datasets.
264 0 $c 2021
300 $a 1 online resource (457 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: 83-02, Section: B.
500 $a Includes supplementary digital materials.
500 $a Advisor: Patel, Chirag J.
502 $a Thesis (Ph.D.)--Harvard University, 2021.
504 $a Includes bibliographical references
520 $a The world population is aging, leading to a rise in the prevalence of age-related diseases such as cardiovascular disease and cancer. In parallel to treating the diseases, an attractive idea is to address the problem at its root by slowing aging. In contrast to a person's chronological age, which solely measures the time since their birth, biological age represents the state of the person's body, is the true underlying cause of age-related diseases and could potentially be reduced by rejuvenating therapies. Training machine learning algorithms to predict chronological age from biomedical datasets yields biological age predictors. Such predictors, such as the DNA methylation aging clock, can be used to further our understanding of aging as a biological process, to identify new mechanisms or lifestyle changes susceptible to slow down aging, and to assess the efficiency of upcoming rejuvenating therapies. A large number of biological age predictors have already been built on various biomedical datasets such as brain MRI images, full body X-rays and blood samples, but it is currently largely unknown whether these predictors all capture a single, central biological aging process, or if each predictor captures a particular dimension of the complex and multi-faceted process that is aging. In other words, can a forty-year-old participant have the arteries of a fifty-year-old person, and the musculoskeletal health of a thirty-year-old person? In the following, we studied how the correlation structure between blood and anthropometric biomarkers changes with age (Chapter 1), and we predicted infant's age from their gut microbiome with a R2 of 62.5±2.1% (Chapter 2). Finally, (Chapter 3), we leveraged 676,787 samples from 502,211 UK Biobank participants to build 331 age predictors on scalar biomarkers (e.g. blood samples), time series (e.g. electrocardiograms), images (e.g. full body X-rays) and videos (heart MRI). We conclude that, although the different aging dimensions are similarly associated with environmental and socioeconomic variables, they are both phenotypically and genetically largely uncorrelated with each other (respective average correlations of .139±.090 and .104±.149), highlighting the multidimensionality of the aging process.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Bioinformatics. $3 553671
650 4 $a Artificial intelligence. $3 516317
650 4 $a Statistics. $3 517247
650 4 $a DNA methylation. $3 3560639
650 4 $a Datasets. $3 3541416
650 4 $a Neural networks. $3 677449
650 4 $a Electrocardiography. $3 773588
650 4 $a X-rays. $3 604695
653 $a Aging
653 $a Artificial intelligence
653 $a Data science
653 $a Deep learning
653 $a Genetics
653 $a Machine learning
655 7 $a Electronic books. $2 lcsh $3 542853
690 $a 0715
690 $a 0800
690 $a 0463
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a Harvard University. $b Systems Biology. $3 2096528
773 0 $t Dissertations Abstracts International $g 83-02B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28498454 $z click for full text (PQDT)