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Machine Learning Applications in Gen...
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University of Massachusetts Boston.
Machine Learning Applications in Genomics, Protein Folding and Protein-Protein Interactions.
紀錄類型:
書目-語言資料,手稿 : Monograph/item
正題名/作者:
Machine Learning Applications in Genomics, Protein Folding and Protein-Protein Interactions./
作者:
Farhoodi, Roshanak.
面頁冊數:
1 online resource (159 pages)
附註:
Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.
Contained By:
Dissertation Abstracts International79-04B(E).
標題:
Computer science. -
電子資源:
click for full text (PQDT)
ISBN:
9780355562583
Machine Learning Applications in Genomics, Protein Folding and Protein-Protein Interactions.
Farhoodi, Roshanak.
Machine Learning Applications in Genomics, Protein Folding and Protein-Protein Interactions.
- 1 online resource (159 pages)
Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.
Thesis (Ph.D.)--University of Massachusetts Boston, 2017.
Includes bibliographical references
The field of machine learning, which aims to develop computer algorithms that improve with experience, has widely assisted scientists in understanding of a vast and diverse array of biological phenomena in recent years. Through the analysis of large and complex datasets by efficient and intelligent algorithms, huge advancements have been made in understanding the biological processes taking place in the cell and the underlying causes of many diseases and abnormalities. Consequently the development of new drugs and treatments have become possible.
Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018
Mode of access: World Wide Web
ISBN: 9780355562583Subjects--Topical Terms:
573171
Computer science.
Index Terms--Genre/Form:
554714
Electronic books.
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Machine Learning Applications in Genomics, Protein Folding and Protein-Protein Interactions.
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This thesis presents machine learning solutions for three biological problems. The first problem is focused on building models to predict the structural similarity of a docked protein complex to its native form. Using a set of physico-chemical features and evolutionary conservation, these models not only rank candidate complexes relative to each other, but also outperform the built-in scoring functions of the docking programs used to generate the complexes. The second problem studies how point mutation can impact the structure and consequently the stability of a protein by employing machine learning methods to predict the change in the free energy of the protein. This approach, which has the potential of providing insight on the effects of multiple mutations of amino acids besides single mutations, does not require costly calculations of energy functions that rely on atomic-level statistical mechanics and molecular energetics. In the third part of this work, a method to identify reads from paired-end sequencing data containing inter-chromosomal translocation or insertion breakpoints is proposed. The huge search space in this problem is examined by applying a distance-preserving embedding algorithm to solve the approximate nearest neighbor problem. Experimental validation and comparison with similar existing methods shows the advantages of this approach in detecting breakpoints efficiently and accurately.
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