How does gene expression clustering work?

By Patrik D'haeseleer. Nature Biotechnology, 2005

557 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/0e67dd3f5de91e7ba4d02e2ef3e146b9218befbe

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Citations in the corpus

Gene co-expression analysis for functional classification and gene–disease predictions (2017, methodology): “Many clustering methods are available, including k-means clustering and hierarchal clustering, and are discussed in detail in [33].”
A deep learning approach to antibiotic discovery (2020, methodology): “The use of Euclidian distance has been considered as the most appropriate to cluster log-ratio data 871 (D'haeseleer, 2005).”
A Deep Learning Approach to Antibiotic Discovery. (2020, unknown): “The use of Euclidian distance has been considered as the most appropriate to cluster log-ratio data (D’haeseleer, 2005).”
Analyzing the Longitudinal K-12 Grading Histories of Entire Cohorts of Students: Grades, Data Driven Decision Making, Dropping out and Hierarchical Cluster Analysis. (2010, methodology): “here is due to its demonstrated success in the past in identifying patterns predictive of overall outcomes from similar types of datasets (Bowers, 2007; D'haeseleer, 2005; Eisen et al., 1998; Quackenbush, 2006; Romesburg, 1984).”
Efficient algorithms for accurate hierarchical clustering of huge datasets: tackling the entire protein space (2008, methodology): “UPGMA is arguably the most popular hierarchical clustering algorithm in use to date, especially for gene expression (D’haeseleer, 2005), and reconstruction of evolutionary trees (Durbin et al.”
Applications of machine learning in animal behaviour studies (2017, unknown): “complete or average) need to be defined (D'haeseleer, 2005).”