A hierarchical unsupervised growing neural network for clustering gene expression patterns

By Javier Herrero, Alfonso Valencia, Joaquín Dopazo. 2001

666 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Molecular Biology

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. Björn Schuller
2. Stefanos P. Zafeiriou
3. Konstantinos Bousmalis
4. Aidong Zhang
5. Siva Sivaganesan
6. Daxin Jiang
7. Mario Medvedovic
8. Chun Tang

In Computer Networks and Communications

1. Aidong Zhang
2. Björn Schuller
3. Daxin Jiang
4. Chun Tang

In Molecular Biology

1. Aidong Zhang
2. Björn Schuller
3. Siva Sivaganesan
4. Daxin Jiang
5. Stefanos P. Zafeiriou
6. Mario Medvedovic
7. Chun Tang
8. Konstantinos Bousmalis

Citations in the corpus

• A Deep Matrix Factorization Method for Learning Attribute Representations (2015, methodology): “By initializing a Deep Semi-NMF with the weights learned with Deep WSF from the small labelled dataset we can leverage all the information we have and allow our unsupervised model to uncover better representations for our initial data on the task of clustering faces.”
• Cluster analysis for gene expression data: a survey (2004, methodology): “The neuron training process of SOM provides a relatively more robust approach than K-means to the clustering of highly noisy data [62, 29].”
• Bayesian infinite mixture model based clustering of gene expression profiles (2002, background): “On the other hand, usual distance measures such as Euclidian distance and similarity measures such as correlation coefficient are meaningful only in comparison to their distribution under the no-structure assumption (Lukashin and Fuchs, 2001; Herrero et al., 2001).”