Tissue classification with gene expression profiles
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
Exploiting hierarchical domain structure to compute similarity
ACM Transactions on Information Systems (TOIS)
Biological cluster validity indices based on the gene ontology
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
Tumor markers are substances, usually proteins that can be found in the blood, urine, stool, tumor tissue and more recently DNA changes, which are produced by the body in response to cancer growth. Thus far, more than 20 different tumor markers have been identified where some of them are specific for a particular type of cancer, while others are associated with several cancer types. The problem of tumor profiling has been extensively studied by the bioinformatics community. Although tumor classification has improved nowadays, there has been no general approach for identifying new cancer classes or for assigning tumors to known classes. In this paper we describe a novel strategy for tumor classification by using Growing Hierarchical Self-Organizing map (GHSOM) since it is able to weigh the contribution of each marker according to its relatedness with other tumor markers as well as handles highly skewed tumor marker expressions well. In the end, experiments are conducted to further demonstrate the feasibility and efficiency of tumor classification approach which provide valuable contribution in the field of oncology and cancer diseases and will be as a guide for the identification of these diseases.