Application of Neural Network to Gene Expression Data for Cancer Classication
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Abstract:
The goal of this work is to explore the use of gene expression data (ged) in discriminating two types of very similar cancers- acute myeloid leukemia (AML) and acute lymphoblastic leukemia(ALL). Classi cation results are reported in [1] using methods other than neural networks. Here, we explore the role of the feature vector in classication. Each feature vector consists of 6817 elements which are gene expression data for 6817 genes. We show in this preliminary experiment that learning using neural network is possible when the input vector contains the correct number of gene expression data. This result is very promising because of the nature of the data (available in large amount and more new information becomes available with better technology and better understanding of the problem). Thus, it is absolutely essential to employ automated recognition system that has learning capability. 1
Citations
| 1938 | Neural Networks - A Comprehensive Foundation – Haykin - 1999 |
| 24 | Molecular classi of cancer: class discovery and class prediction by gene expression monitoring – Golub, Slonim, et al. - 1999 |
| 1 | 2000. Neural Network and Arti Intelligence for Biomedical Engineering – Hudson, Cohen |
