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Deriving Homogeneous Subsets from Gene Sets by Exploiting the Gene Ontology
Volume 34, Issue 2 (2023), pp. 357–386
Pub. online: 22 May 2023      Type: Research Article      Open accessOpen Access
Received
1 June 2022
Accepted
1 May 2023
Published
22 May 2023

Abstract

The Gene Ontology (GO) knowledge base provides a standardized vocabulary of GO terms for describing gene functions and attributes. It consists of three directed acyclic graphs which represent the hierarchical structure of relationships between GO terms. GO terms enable the organization of genes based on their functional attributes by annotating genes to specific GO terms. We propose an information-retrieval derived distance between genes by using their annotations. Four gene sets with causal associations were examined by employing our proposed methodology. As a result, the discovered homogeneous subsets of these gene sets are semantically related, in contrast to comparable works. The relevance of the found clusters can be described with the help of ChatGPT by asking for their biological meaning. The R package BIDistances, readily available on CRAN, empowers researchers to effortlessly calculate the distance for any given gene set.

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Biographies

Stier Quirin

Q. Stier received his bachelor in mathematics at the University of Erlangen in 2017 and his master in data science at the University of Marburg in 2021. His master thesis investigated time series forecasting using wavelet analysis comparing it to popular current state-of-the-art methods. Currently, he is pursuing a PhD in artificial intelligence focusing on interpretable techniques applicable for human-in-the-loop processes at the University of Marburg.

Thrun Michael C.
https://orcid.org/0000-0001-9542-5543
mthrun@informatik.uni-marburg.de

Priv.-Doz. Dr. habil. M.C. Thrun received his diploma in physics (2014) and his doctorate in data science (2017) at the Philipps-University Marburg under the chair of Databionics Prof. Dr. habil. Alfred H.G. Ultsch. Afterwards, he worked for almost two years as a Big Data Scientist for an international manufacturer. He is the author of the book “Projection-Based Clustering through Self-Organization and Swarm Intelligence”. His team specializes in explainable artificial intelligence, predicting time series and knowledge discovery using methods borrowed from nature. Additionally, they are researching the topic of recognizing and explaining diseases. In 2022, he received his habilitation in informatics at the Philipps-University Marburg with a thesis about explainable artificial intelligence and a colloquium about reinforcement learning in praxis. Currently, Thrun holds a position for lecturing on databionic methods of artificial intelligence, time series analysis and knowledge discovery in the Data Science program at the Philipps University of Marburg.


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Keywords
gene ontology gene analysis cluster analysis knowledge base ChatGPT

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