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Evaluation and Improvement of Genome-Wide Human Protein-Protein Interaction Prediction

DUNHAM, BRANDAN (2023) Evaluation and Improvement of Genome-Wide Human Protein-Protein Interaction Prediction. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Background: Proteins are biological macromolecules that interact with each other, performing various functions and regulate many biological processes, making them vital to many types of biological research. However, as many protein-protein interactions (PPIs) remain unknown, and interacting protein pairs are rare among all protein pairs, it is important for researchers to find ways to predict novel interactors with high precision, reducing experimental costs by prioritizing likely interactors.

Methods: In this thesis, we evaluate thirty-six previously published methods, and assess their suitability for predicting novel interactions. We analyze the ability of these methods to predict PPIs of proteins not used during training. This avoids a problem we hypothesized may exist in most methods, especially those that rely on protein sequence derived features. Similarly, we hypothesized removing this problem could yield better, more generalizable predictions when using annotation-based features for predicting interactions.

Results: In our analyses, we found that most sequence-based models were unable to accurately predict interactions where the proteins were not in the training set. We obtained better results when using features that did not rely on primary sequence information, and showed that the models that performed well on unseen proteins were better at predicting proteome-wide interactions.

Discussion: Our results show that models generated to maximize precision when predicting on protein pairs composed of proteins not used during training are better at making predictions proteome-wide. These models predict more validated PPIs from other data sources, and are less biased towards predicting hubs, than models trained in the traditional way.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
DUNHAM, BRANDANbrd86@pitt.edubrd860000-0002-2433-801X
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairHochheiser, Harryharryh@pitt.eduharryh0000-0001-8793-9982
Thesis AdvisorGanapathiraju, Madhavi K.madhavi@pitt.edumadhavi0000-0002-3825-0924
Committee MemberVisweswaran, Shyamshv3@pitt.edushv30000-0002-2079-8684
Committee MemberKlein-Seetharaman, Judithjudith.klein-seetharaman@asu.edu0000-0002-4892-6828
Date: 12 April 2023
Date Type: Publication
Defense Date: 18 July 2022
Approval Date: 12 April 2023
Submission Date: 18 August 2022
Access Restriction: 1 year -- Restrict access to University of Pittsburgh for a period of 1 year.
Number of Pages: 259
Institution: University of Pittsburgh
Schools and Programs: School of Medicine > Biomedical Informatics
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: protein-protein interactions, prediction, machine learning, interactome
Related URLs:
Date Deposited: 12 Apr 2023 13:27
Last Modified: 12 Apr 2024 05:15


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