Cluster analysis for gene expression data: a survey

By Daxin Jiang, Chun Tang, Aidong Zhang. IEEE Transactions on Knowledge and Data Engineering, 2004

1,349 citations recorded by the source corpus.

Subfields: Information Systems, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/c490038ca62193948f746abc94f623792bf51348

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

In Molecular Biology

Citations in the corpus

Finding large average submatrices in high dimensional data (2009, background): “Unsupervised exploratory analysis plays an important role in the study of large, high-dimensional datasets that arise in a variety of applications, including gene expression microarrays.”
How does gene expression clustering work? (2005, methodology): “Jiang et al. 4 provide a survey of methods used specifically for gene expression data.”
A survey on nature inspired metaheuristic algorithms for partitional clustering (2014, background): “[29], in medical sciences to identify diseases from a group of patient reports and genomic studies [30], in library sciences for grouping books based upon the content [32], etc.”
Community Detection in General Stochastic Block models: Fundamental Limits and Efficient Algorithms for Recovery (2015, background): “…communities allows one to find like-minded people in social networks [1], [2], to improve recommendation systems [3], [4], to segment or classify images [5], [6], to detect protein complexes [7], [8], to find genetically related sub-populations [9], [10], or to discover new tumor subclasses [11].”
A Comparison Study on Similarity and Dissimilarity Measures in Clustering Continuous Data (2015, methodology): “Pearson correlation is widely used in clustering gene expression data [33,36,40].”
Clique Relaxations in Social Network Analysis: The Maximum k-Plex Problem (2011, neutral): “These drawbacks are pointed out and the models are appropriately redeﬁned in [7], as described in Section 2.”
Privacy-Preserving Stochastic Gradual Learning (2018, background): “Unfortunately, large-scale sensitive data are ubiquitous in the real world, such as electronic health record [6], mobile app information [7,8], and genome-wide association database [9,10,11].”
Survey of clustering algorithms (2005, background): “We offer more detailed discussion on how to identify appropriate number of clusters, which is particularly important in cluster validity, in the last part of the section.”