Deep convolution neural network model to predict relapse in breast cancer
Jha, Alokkumar Verma, Ghanshyam Khan, Yasar Mehmood, Qaiser Rebholz-Schuhmann, Dietrich Sahay, Ratnesh
Jha, Alokkumar
Verma, Ghanshyam
Khan, Yasar
Mehmood, Qaiser
Rebholz-Schuhmann, Dietrich
Sahay, Ratnesh
Loading...
Repository DOI
Publication Date
2018-12-17
Type
Conference Paper
Downloads
Citation
Jha, Alokkumar, Verma, Ghanshyam, Khan, Yasar, Mehmood, Qaiser, Rebholz-Schuhmann, Dietrich, & Sahay, Ratnesh. (2018). Deep convolution neural network model to predict relapse in breast cancer. Paper presented at the 17th IEEE International Conference on Machine Learning and Applications (ICMLA 2018), Orlando, Florida, USA, 17-20 December, doi: 10.1109/ICMLA.2018.00059
Abstract
A mishap in anti-cancer drug distribution is critical in breast cancer patients due to poor prediction model to identify the treatment regime in ER+ve and ER-ve (Estrogen Receptor (ER)) patients. The traditional method for the prediction depends on the change in expression across the normal-disease pair. However, it certainly misses the multidimensional aspect and underlying cause of relapse, such as various mutations, drug dosage side effects, methylation, etc. In this paper, we have developed a multi-layer neural network model to classify multidimensional genomics data into their similar annotation group. Further, we used this multi-layer cancer genomics perceptron for annotating differentially expressed genes (DEGs) to predict relapse based on ER status in breast cancer. This approach provides multivariate identification of genes, not just by differential expression, but, cause-effect of disease status due to drug overdosage and genomics-driven drug balancing method. The multi-layered neural network model, where each layer defines the relationship of similar databases with multidimensional knowledge. We illustrate that the use of multilayer knowledge graph with gene expression data for training the deep convolution neural network stratify the patient relapse and drug dosage along with underlying molecular properties.
Publisher
IEEE
Publisher DOI
10.1109/ICMLA.2018.00059
Rights
Attribution-NonCommercial-NoDerivs 3.0 Ireland