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Prediction of NSCLC recurrence from microarray data with GEP

journal contribution
posted on 2017-06-01, 00:00 authored by Russul Al-AnniRussul Al-Anni, Jingyu HouJingyu Hou, R D Abdu-aljabar, Yong XiangYong Xiang
Lung cancer is one of the deadliest diseases in the world. Non-small cell lung cancer (NSCLC) is the most common and dangerous type of lung cancer. Despite the fact that NSCLC is preventable and curable for some cases if diagnosed at early stages, the vast majority of patients are diagnosed very late. Furthermore, NSCLC usually recurs sometime after treatment. Therefore, it is of paramount importance to predict NSCLC recurrence, so that specific and suitable treatments can be sought. Nonetheless, conventional methods of predicting cancer recurrence rely solely on histopathology data and predictions are not reliable in many cases. The microarray gene expression (GE) technology provides a promising and reliable way to predict NSCLC recurrence by analysing the GE of sample cells. This study proposes a new model from GE programming to use microarray datasets for NSCLC recurrence prediction. To this end, the authors also propose a hybrid method to rank and select relevant prognostic genes that are related to NSCLC recurrence prediction. The proposed model was evaluated on real NSCLC microarray datasets and compared with other representational models. The results demonstrated the effectiveness of the proposed model.

History

Journal

IET systems biology

Volume

11

Issue

3

Pagination

77 - 86

Publisher

Institution of Engineering and Technology

Location

Stevenage, Eng.

ISSN

1751-8849

eISSN

1751-8857

Language

eng

Publication classification

C Journal article; C1 Refereed article in a scholarly journal

Copyright notice

2017, Institution of Engineering and Technology