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Machine Learning Approaches for Extr...

Wang, Tongxin.

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Machine Learning Approaches for Extracting Biological Insights from Heterogeneous Omics Data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine Learning Approaches for Extracting Biological Insights from Heterogeneous Omics Data./
作者:	Wang, Tongxin.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	188 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-01, Section: B.
Contained By:	Dissertations Abstracts International83-01B.
標題:	Computer science. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28541819
ISBN:	9798516912948

Machine Learning Approaches for Extracting Biological Insights from Heterogeneous Omics Data.
Wang, Tongxin.

Machine Learning Approaches for Extracting Biological Insights from Heterogeneous Omics Data. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 188 p.

Source: Dissertations Abstracts International, Volume: 83-01, Section: B.

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

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

With the breakthrough in biomedical technologies over the last decades, the field of biomedical research has entered the "big data" era. Rapid advancement in high-throughput omics technologies has generated a tremendous amount of data that requires incorporating machine learning algorithms for effective analysis. With the consistent evolution in omics technologies, the data being generated are not only growing in scale but also in complexity and heterogeneity. While the ever-changing and ever-growing omics data keep bringing new computational challenges that demand new computation tools, they also bring new opportunities for a deeper and more comprehensive view into the underlying biomedical problems. To address the computational challenges brought by the continuous development of omics technologies, we focus on developing data-driven approaches that utilize machine learning for better exploiting the omics data for biological insights. Specifically, following the transformation of omics technologies, we develop methodologies, frameworks, and algorithms for omics data with different complexity and heterogeneity, ranging from single-omics to multi-omics data, as well as from bulk sequencing to single-cell sequencing data.

ISBN: 9798516912948Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Big data

Machine Learning Approaches for Extracting Biological Insights from Heterogeneous Omics Data.
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