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Continuous Tensor Factorization Effectively Reduces Data with Longitudinal Measurements.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Continuous Tensor Factorization Effectively Reduces Data with Longitudinal Measurements./
作者:	Murphy, Madeleine Claire.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
面頁冊數:	48 p.
附註:	Source: Masters Abstracts International, Volume: 83-12.
Contained By:	Masters Abstracts International83-12.
標題:	Bioinformatics. -
ISBN:	9798819391327

Continuous Tensor Factorization Effectively Reduces Data with Longitudinal Measurements.
Murphy, Madeleine Claire.

Continuous Tensor Factorization Effectively Reduces Data with Longitudinal Measurements. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 48 p.

Source: Masters Abstracts International, Volume: 83-12.

Thesis (M.S.)--University of California, Los Angeles, 2022.

This item must not be sold to any third party vendors.

A suite of methods known as tensor decompositions have enabled researchers to explore high-order biological interactions more effectively. These methods are essential for allowing data-analysts to keep up with the rapid generation of high-throughput data the scientific community is now capable of producing. However, few tensor decomposition methods are capable of handling longitudinal data, one of the most common data types in biology. Here, we have developed a tensor factorization method capable of factoring data with a continuous mode, to combat the issue of reducing data with measurements taken over time. The method is capable of capturing a large amount of variance in both simulated and published longitudinal immune response data. The method additionally reproduces specific temporal dynamics in the data with high accuracy. Finally, it allows us to better visualize the key biological patterns and their interactions with time across the entire data-set.

ISBN: 9798819391327Subjects--Topical Terms:

553671
Bioinformatics.
Subjects--Index Terms:

SARS‐CoV‐2

Continuous Tensor Factorization Effectively Reduces Data with Longitudinal Measurements.
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520 $a A suite of methods known as tensor decompositions have enabled researchers to explore high-order biological interactions more effectively. These methods are essential for allowing data-analysts to keep up with the rapid generation of high-throughput data the scientific community is now capable of producing. However, few tensor decomposition methods are capable of handling longitudinal data, one of the most common data types in biology. Here, we have developed a tensor factorization method capable of factoring data with a continuous mode, to combat the issue of reducing data with measurements taken over time. The method is capable of capturing a large amount of variance in both simulated and published longitudinal immune response data. The method additionally reproduces specific temporal dynamics in the data with high accuracy. Finally, it allows us to better visualize the key biological patterns and their interactions with time across the entire data-set.
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