Artificial Intelligence (AI) and machine learning-driven automation of complex, neurobiological model reduction: A framework based on deep learning, ensemble learning, and sensitivity analysis methodologies : A thesis submitted in partial fulfilment of the requirements for the Degree of Doctor of Philosophy in Computer Science at Lincoln University

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Title

Artificial Intelligence (AI) and machine learning-driven automation of complex, neurobiological model reduction: A framework based on deep learning, ensemble learning, and sensitivity analysis methodologies : A thesis submitted in partial fulfilment of the requirements for the Degree of Doctor of Philosophy in Computer Science at Lincoln University

Content partner

Lincoln University

Collection

Lincoln University Research Archive

Description

Managing complexity is a key challenge in systems biology modelling. While detailed models can provide valuable insights into specific biological processes, they can become computationally intensive and challenging to interpret. Conversely, overly simplistic models may lack accuracy and fail to capture essential aspects of the system's behaviour. Hence, this study focuses on reducing the complexity of the models in systems biology while enhancing accuracy, efficiency, and interpretability. T...

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Format

Research Paper

Research format

Thesis

Thesis level

Doctoral

Date created

2023

Creator

Kumara Pathirannahalage, Samantha Dileshani Kumara Pathirana

URL

https://hdl.handle.net/10182/16888

Related subjects

machine learning / ensemble learning / sensitivity analysis / model reduction / complexity reduction / neurobiology models / deep learning / systems biology modelling / artificial intelligence / computational models / Artificial intelligence not elsewhere classified / Deep learning / Computational neuroscience (incl. mathematical neuroscience and theoretical neuroscience) / Modelling and simulation

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