Artificial Neural Networks approaches for multidimensional classification of Acute Lymphoblastic Leukemia gene expression data
Article
Zong, N, Adjouadi, M, Ayala, M. (2005). Artificial Neural Networks approaches for multidimensional classification of Acute Lymphoblastic Leukemia gene expression data
. 2(8), 1071-1078.
Zong, N, Adjouadi, M, Ayala, M. (2005). Artificial Neural Networks approaches for multidimensional classification of Acute Lymphoblastic Leukemia gene expression data
. 2(8), 1071-1078.
Accurate classification of human blood cells plays a decisive role in the diagnosis and treatment of diseases. Artificial Neural Networks (ANNs) have been consistently used as a trusted classification tool for this type of analysis. In the present case study, two approaches are implemented on two different parametric data clusters in a multidimensional space using ANNs trained with cross-validation. Beckman-Coulter Corporation supplied flow cytometry data of numerous patients that were used as training sets for the first approach to exploit the physiological characteristics of the different blood cells provided. The goal was to establish a programming tool for the identification of different white blood cell categories of a given blood sample and provide information to medical doctors in the form of diagnostic references for the specific disease state that is considered for this study, namely Acute Lymphoblastic Leukemia (ALL). Successful initial results of this first approach have been published. The second approach is focusing on the gene expression profiling of ALL to classify its six subtypes. Generated by the oligonucleotide microarrays, this data provides additional insights into the biology underlying the clinical differences between these leukemia subgroups. With the application of the hypothesis space, along with the learning bias, the system is also trained to assess the inherent problem of data overlap and be able to recognize abnormal blood cell patterns. An analysis of the systems regarding computational load and receiver-operating characteristic (ROC) was conducted. The algorithms as proposed provide solutions to data overlap from our initial results. And by applying ANNs, the classification accuracy of the first approach is remarkably improved up to 100% and 92% for the second approach.
