Bayesian infinite mixture model based clustering of gene expression profiles

By Mario Medvedovic, Siva Sivaganesan. Bioinform., 2002

331 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/31e87e1725ea936e44b472dc84d611ea196ad002

How researchers see this paper

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

In Artificial Intelligence

1. David B. Dunson
2. Alan E. Gelfand
3. Enrique ter Horst
4. Abel Rodríguez
5. Abel Rodríguez
6. Mark F. J. Steel
7. Jim E. Griffin
8. Lawrence Carin

In Molecular Biology

1. Jim E. Griffin
2. Enrique ter Horst
3. Abel Rodríguez
4. David B. Dahl
5. Alan E. Gelfand
6. Ying-jie Tian
7. Mark F. J. Steel
8. Zoubin Ghahramani

In Statistics and Probability

1. David B. Dunson
2. Mark F. J. Steel
3. Jim E. Griffin
4. Abel Rodríguez
5. Alan E. Gelfand
6. Bani K. Mallick
7. Enrique ter Horst
8. Abel Rodríguez

Citations in the corpus

• Order-Based Dependent Dirichlet Processes (2006, background): “…2002), animal breeding (van der Merwe and Pretorius 2003), survival analysis (Doss and Huffer 2003), directional data (Ghosh, Jammalamadaka, and Tiwari 2003), meta-analysis (Chung, Dey, and Jang 2002), genetics (Medvedovic and Sivaganesan 2002) and density estimation (Hansen and Lauritzen 2002).”
• Bayesian dynamic density estimation (2008, background): “Some examples of recent applications of the Dirichlet processes include finance, econometrics, epidemiology, genetics and survival analysis among others (Müller et al. 2004; Kacperczyk et al. 2004; Chib and Hamilton 2002; Hirano 2002; Dunson 2005; Medvedovic and Sivaganesan 2002; Dunson et al. 2007a”
• Bayesian Model Averaging: A Systematic Review and Conceptual Classification (2015, background): “Some noteworthy examples in the literature include Medvedovic and Sivaganesan (2002) that elicited model priors by the expected behavior of the cell cultures under study, the analytical considerations of the Bayesian networks of interest in Friedman and Koller (2003), the combination of multiple…”
• A Comprehensive Survey of Clustering Algorithms (2015, background): “For more detailed information about this kind of clustering algorithms, you can refer to [70–75].”
• Multiple Hypothesis Testing by Clustering Treatment Effects (2007, methodology): “With regard to clustering, model-based methods (Fraley and Raftery 2002; Yeung, Fraley, Murua, Raftery, and Ruzzo 2001; Medvedovic and Sivaganesan 2002) offer certain advantages, although they have had less impact in genomics than more heuristic schemes (Eisen, Spellman, Brown, and Botstein 1998).”
• Functional clustering by Bayesian wavelet methods (2006, methodology): “Medvedovic and Sivaganesan (2002) used the Dirichlet-process- (DP) based infinite mixture model to overcome these deficiencies.”
• Probabilistic machine learning and artificial intelligence (2015, methodology): “Dirichlet processes have also been used for clustering gene-expression patterns 38,39 .”
• An Introduction to Bayesian Inference via Variational Approximations (2011, methodology): “This includes applications in statistics (e.g., Escobar and West 1995), computer science (e.g., Teh et al. 2006), biology (e.g., Kottas, Branco, and Gelfand 2002; Medvedovic and Sivaganesan 2002), and political science (e.g., Quinn and Spirling 2010).”
• The Nested Dirichlet Process (2008, background): “…DP include finance (Kacperczyk, Damien, and Walker 2003), econometrics (Chib and Hamilton 2002; Hirano 2002), epidemiology (Dunson 2005), genetics (Medvedovic and Sivaganesan 2002; Dunson, Herring, and Mulheri-Engel 2007a), medicine (Kottas, Branco, and Gelfand 2002; Bigelow and Dunson 2007), and…”