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Benchmarking of Graph Autoencoder models for Gene Regulatory Network inference with prior knowledge

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dc.contributor.advisor Scialdone, Antonio
dc.contributor.author PATERIA, HARSHIT
dc.date.accessioned 2023-05-17T08:17:43Z
dc.date.available 2023-05-17T08:17:43Z
dc.date.issued 2023-05
dc.identifier.citation 42 en_US
dc.identifier.uri http://dr.iiserpune.ac.in:8080/xmlui/handle/123456789/7885
dc.description.abstract This thesis explores the benchmarking of graph auto-encoders for inferring gene regulatory networks (GRNs) using prior knowledge of known GRNs. Gene regulatory networks are crucial for understanding gene expression and their regulation in various biological processes. However, obtaining experimentally validated GRNs can be expensive and time-consuming. In this thesis, we propose using graph auto-encoders, a type of neural network, to learn the underlying structure of GRNs from gene expression data. We evaluate different types of graph auto-encoders, including the standard Graph Auto-Encoder (GAE), Variational Graph Auto-Encoder (VGAE), Adversarially Regularized Graph Auto-Encoder (ARGA), and Adversarially Regularized Variational Graph Auto-Encoder (ARGVA) to infer GRNs from prior knowledge. Implementation of a trainable decoder shows better results in comparison to the standard. We compare the performance of each autoencoder with respect to the performance and stability of inferred GRNs. This study demonstrates the potential of different architectures of graph auto-encoders for inferring gene regulatory networks using prior knowledge to compare performance and stability in gene network inference. en_US
dc.language.iso en en_US
dc.subject Graph Autoencoder en_US
dc.subject Gene Regulatory Network en_US
dc.title Benchmarking of Graph Autoencoder models for Gene Regulatory Network inference with prior knowledge en_US
dc.type Thesis en_US
dc.description.embargo One Year en_US
dc.type.degree BS-MS en_US
dc.contributor.department Dept. of Data Science en_US
dc.contributor.registration 20181032 en_US


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  • MS THESES [1705]
    Thesis submitted to IISER Pune in partial fulfilment of the requirements for the BS-MS Dual Degree Programme/MSc. Programme/MS-Exit Programme

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