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Causal Reasoning and Machine Learnin...

University of Massachusetts Boston.

Causal Reasoning and Machine Learning Models for Cellular Regulatory Mechanisms.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Causal Reasoning and Machine Learning Models for Cellular Regulatory Mechanisms./
作者:	Fakhry, Carl Tony.
面頁冊數:	1 online resource (140 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
Contained By:	Dissertation Abstracts International79-10B(E).
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9780438003620

Causal Reasoning and Machine Learning Models for Cellular Regulatory Mechanisms.
Fakhry, Carl Tony.

Causal Reasoning and Machine Learning Models for Cellular Regulatory Mechanisms. - 1 online resource (140 pages)

Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.

Thesis (Ph.D.)--University of Massachusetts Boston, 2018.

Includes bibliographical references

In this dissertation, we tackle problems in gene regulation and distance metric learning. In the first part of this thesis, we present three novel approaches for modeling transcriptional and post-transcriptional gene regulatory mechanisms. First, we propose a causal reasoning model for inferring upstream regulators of gene expression, including transcriptional regulators. Second, we propose a model for predicting small RNAs (sRNAs) in bacterial species that act as post-transcriptional regulators of the global regulator CsrA. Third, we propose a generalization of genome-wide association study (GWAS) over regulatory networks to identify functional pathways that are associated with a complex trait. Finally, in the second part of this thesis, we present a reformulation of the distance metric learning problem. All of our methods achieve good performance, are computationally efficient and are implemented in open-source R packages which can be installed from public repositories.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780438003620Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Causal Reasoning and Machine Learning Models for Cellular Regulatory Mechanisms.
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245 1 0 $a Causal Reasoning and Machine Learning Models for Cellular Regulatory Mechanisms.
264 0 $c 2018
300 $a 1 online resource (140 pages)
336 $a text $b txt $2 rdacontent
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500 $a Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
500 $a Adviser: Ping Chen.
502 $a Thesis (Ph.D.)--University of Massachusetts Boston, 2018.
504 $a Includes bibliographical references
520 $a In this dissertation, we tackle problems in gene regulation and distance metric learning. In the first part of this thesis, we present three novel approaches for modeling transcriptional and post-transcriptional gene regulatory mechanisms. First, we propose a causal reasoning model for inferring upstream regulators of gene expression, including transcriptional regulators. Second, we propose a model for predicting small RNAs (sRNAs) in bacterial species that act as post-transcriptional regulators of the global regulator CsrA. Third, we propose a generalization of genome-wide association study (GWAS) over regulatory networks to identify functional pathways that are associated with a complex trait. Finally, in the second part of this thesis, we present a reformulation of the distance metric learning problem. All of our methods achieve good performance, are computationally efficient and are implemented in open-source R packages which can be installed from public repositories.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Artificial intelligence. $3 559380
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
690 $a 0800
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of Massachusetts Boston. $b Computer Science. $3 1179593
773 0 $t Dissertation Abstracts International $g 79-10B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10786109 $z click for full text (PQDT)

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