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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 XiangLung 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 biologyVolume
11Issue
3Pagination
77 - 86Publisher
Institution of Engineering and TechnologyLocation
Stevenage, Eng.Publisher DOI
ISSN
1751-8849eISSN
1751-8857Language
engPublication classification
C Journal article; C1 Refereed article in a scholarly journalCopyright notice
2017, Institution of Engineering and TechnologyUsage metrics
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No categories selectedKeywords
cancergeneticslab-on-a-chiplungpatient diagnosisScience & TechnologyLife Sciences & BiomedicineCell BiologyMathematical & Computational BiologyNSCLC recurrence predictionmicroarray dataGE programmingnonsmall cell lung cancercancer recurrencehistopathology datamicroarray gene expressionprognostic genesCELL LUNG-CANCERNEURAL-NETWORKSBREAST-CANCERSVM-RFEGENECLASSIFICATIONSELECTIONALGORITHMSURVIVALRULES
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