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A Novel Ensemble Method using Signed and Unsigned Graph Convolutional Networks for Predicting Mechanisms of Action of Small Molecules from Gene Expression Data

Karim, Rashid Saadman
http://orcid.org/0000-0002-5179-5259
http://rave.ohiolink.edu/etdc/view?acc_num=ucin164977133267139

Abstract Details

2022, PhD, University of Cincinnati, Engineering and Applied Science: Computer Science and Engineering.
Identification of the mechanism of action (MoA) of a small molecule which causes pharmacological effects on cellular networks governing gene expression levels is an important field of study for the purpose of drug development and repurposing. While gene expression can be used for the prediction of small molecule MoA using traditional machine learning algorithms, these algorithms do not consider the underlying complexity of cellular level biological networks driving gene expression. In particular, capturing predictive features from the polarity of interaction in cell signaling networks where nodes in the network either activate or inhibit other nodes is still a challenging problem for the prediction of drug MoA. We propose an ensemble deep learning meta-algorithm for predicting small molecule MoA from gene expression data using unsigned and signed graph convolutional networks (GCN). We developed a GCN algorithm to extract features from signed networks and combined predictive probabilities with that of an unsigned GCN using stacking. Our ensemble methodology improves the overall predictive capabilities significantly when compared to unsigned or signed GCN.
Mario Medvedovic, Ph.D. (Committee Member)
Gowtham Atluri, Ph.D. (Committee Member)
Ali Minai, Ph.D. (Committee Member)
Jaroslaw Meller, Ph.D. (Committee Member)
Raj Bhatnagar, Ph.D. (Committee Member)
139 p.
Bioinformatics
Graph Convolutional Neural Network; Drug Mechanism of Action Prediction; Ensemble Learning; Unsigned and Signed Networks; Bioinformatics; Deep Learning on Gene Expression Data

Recommended Citations

Citations

  • Karim, R. S. (2022). A Novel Ensemble Method using Signed and Unsigned Graph Convolutional Networks for Predicting Mechanisms of Action of Small Molecules from Gene Expression Data [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin164977133267139

    APA Style (7th edition)

  • Karim, Rashid Saadman. A Novel Ensemble Method using Signed and Unsigned Graph Convolutional Networks for Predicting Mechanisms of Action of Small Molecules from Gene Expression Data. 2022. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin164977133267139.

    MLA Style (8th edition)

  • Karim, Rashid Saadman. "A Novel Ensemble Method using Signed and Unsigned Graph Convolutional Networks for Predicting Mechanisms of Action of Small Molecules from Gene Expression Data." Doctoral dissertation, University of Cincinnati, 2022. http://rave.ohiolink.edu/etdc/view?acc_num=ucin164977133267139

    Chicago Manual of Style (17th edition)

ucin164977133267139
42
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This open access ETD is published by University of Cincinnati and OhioLINK.