Purdue University Graduate School
BioGRNSemble_final.pdf (3.12 MB)

Inferencing Gene Regulatory Networks for Drosophila Eye Development Using an Ensemble Machine Learning Approach

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posted on 2024-04-29, 18:35 authored by Abdul Jawad MohammedAbdul Jawad Mohammed

The primary purpose of this thesis is to propose and demonstrate BioGRNsemble, a modular and flexible approach for inferencing gene regulatory networks from RNA-Seq data. Integrating the GENIE3 and GRNBoost2 algorithms, this ensembles-of-ensembles method attempts to balance the outputs of both models through averaging, before providing a trimmed-down gene regulatory network consisting of transcription and target genes. Using a Drosophila Eye Dataset, we were able to successfully test this novel methodology, and our validation analysis using an online database determined over 3500 gene links correctly detected, albeit out of almost 530,000 predictions, leaving plenty of room for improvement in the future.


Degree Type

  • Master of Science


  • Computer Science

Campus location

  • Fort Wayne

Advisor/Supervisor/Committee Chair

Amal Khalifa

Additional Committee Member 2

Haytham Idriss

Additional Committee Member 3

Jonathan Rusert