Parsing genetic models

Applications of computer vision have seen great success recently, yet there are few approaches dealing with visual illustrations. We propose a collection of computer vision applications for parsing genetic models. Genetic models are a visual illustration often used in the biological sciences literature. These are used to demonstrate how a discovery fits into what is already known about a biological system. A system that determines the interactions present in a genetic model can be valuable to researchers studying such interactions. The proposed system contains three parts. First, a triplet network is deployed to decide whether or not a figure is a genetic model. Second, a popular object detection network YOLOvS is trained to locate regions of interest within genetic models using various deep learning training techniques. Lastly, we propose an algorithm that can infer the relationships between the pairs of genes or textual features present in the genetic model.

Name thesis:degree_name

Master of Science (MSc)

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Ontario Institute of Technology

Year dc:date.issued

2022

Author dc:creator

• Lombardo, Michael Natale

Advisor dc:contributor.advisor

• Qureshi, Faisal Z.

• Diagram understanding
• Diagram detection
• Visual illustrations
• Bioinformatics
• Object detection

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10155/1421

OAI identifier oai:identifier

oai:ontariotechu.scholaris.ca:10155/1421