Explaining protein-protein interactions with knowledge graph-based semantic similarity
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gp-bibliography.bib Revision:1.7954
- @Article{SOUSA:2024:compbiomed,
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author = "Rita T. Sousa and Sara Silva and Catia Pesquita",
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title = "Explaining protein-protein interactions with knowledge
graph-based semantic similarity",
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journal = "Computers in Biology and Medicine",
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volume = "170",
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pages = "108076",
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year = "2024",
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ISSN = "0010-4825",
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DOI = "doi:10.1016/j.compbiomed.2024.108076",
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URL = "https://www.sciencedirect.com/science/article/pii/S0010482524001604",
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keywords = "genetic algorithms, genetic programming, Machine
learning, Explainable artificial intelligence,
Knowledge graph, Semantic similarity, Protein-protein
interaction prediction",
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abstract = "The application of artificial intelligence and machine
learning methods for several biomedical applications,
such as protein-protein interaction prediction, has
gained significant traction in recent decades. However,
explainability is a key aspect of using machine
learning as a tool for scientific discovery.
Explainable artificial intelligence approaches help
clarify algorithmic mechanisms and identify potential
bias in the data. Given the complexity of the
biomedical domain, explanations should be grounded in
domain knowledge which can be achieved by using
ontologies and knowledge graphs. These knowledge graphs
express knowledge about a domain by capturing different
perspectives of the representation of real-world
entities. However, the most popular way to explore
knowledge graphs with machine learning is through using
embeddings, which are not explainable. As an
alternative, knowledge graph-based semantic similarity
offers the advantage of being explainable.
Additionally, similarity can be computed to capture
different semantic aspects within the knowledge graph
and increasing the explainability of predictive
approaches. We propose a novel method to generate
explainable vector representations, KGsim2vec, that
uses aspect-oriented semantic similarity features to
represent pairs of entities in a knowledge graph. Our
approach employs a set of machine learning models,
including decision trees, genetic programming, random
forest and eXtreme gradient boosting, to predict
relations between entities. The experiments reveal that
considering multiple semantic aspects when representing
the similarity between two entities improves
explainability and predictive performance. KGsim2vec
performs better than black-box methods based on
knowledge graph embeddings or graph neural networks.
Moreover, KGsim2vec produces global models that can
capture biological phenomena and elucidate data
biases",
- }
Genetic Programming entries for
Rita T Sousa
Sara Silva
Catia Luisa Santana Calisto Pesquita
Citations