Self-organizing maps in mining gene expression data
Information Sciences: an International Journal
Analysis and visualization of gene expression data using self-organizing maps
Neural Networks - New developments in self-organizing maps
Cluster Analysis for Gene Expression Data: A Survey
IEEE Transactions on Knowledge and Data Engineering
Applications of artificial intelligence in bioinformatics: A review
Expert Systems with Applications: An International Journal
Cluster analysis of gene expression data based on self-splitting and merging competitive learning
IEEE Transactions on Information Technology in Biomedicine
Unsupervised query-based learning of neural networks using selective-attention and self-regulation
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Particle swarm optimization with query-based learning for multi-objective power contract problem
Expert Systems with Applications: An International Journal
Review: Knowledge discovery in medicine: Current issue and future trend
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Gene clustering is very important for extracting underlying biological information of gene expression data. Currently, SOM (self-organizing maps) is known as one of the most popular neural networks applied for gene clustering. However, SOM is sensitive to the initialization of neurons' weights. In this case, biologists may need to spend a lot of time in repeating experiments until they obtain a satisfactory clustering result. In this paper, we apply QBSOM (query-based SOM) to tackle the drawbacks of SOM. We have tested the proposed method by several kinds of real gene expression data. Experimental results show that QBSOM is superior to SOM in not only the time consumed but also the result obtained. Considering the gene clustering result of YF (yeast full) dataset, QBSOM yields 17% less in MSE (mean-square-error) and 68% less in computation cost compared with SOM. Our experiments also indicate that QBSOM is particularly adaptive for clustering high dimensional data such as the gene expression data. It is better than SOM for system convergence.