Identification of Gene Subnetwork Biomarkers of Lung Cancer from RNA-seq Data
Conference proceedings article
ผู้เขียน/บรรณาธิการ
กลุ่มสาขาการวิจัยเชิงกลยุทธ์
รายละเอียดสำหรับงานพิมพ์
รายชื่อผู้แต่ง: Sreebunpeng, Kritsada; Chan, Jonathan H.; Meechai, Asawin;
ผู้เผยแพร่: Hindawi
ปีที่เผยแพร่ (ค.ศ.): 2020
หน้าแรก: 33
หน้าสุดท้าย: 39
จำนวนหน้า: 7
ISBN: 9781450388238
นอก: 0146-9428
eISSN: 1745-4557
ภาษา: English-Great Britain (EN-GB)
บทคัดย่อ
In recent years, the increasing availability of cancer RNA-seq datasets has provided unprecedented information and opportunities for the discovery of biomarkers for cancer. In this study, we tested our previously published Gene Sub-Network-based Feature Selection (GSNFS) method to identify gene-subnetwork biomarkers with RNA-seq-based gene expression data of lung cancer. In addition, five different filter-based feature selection techniques were explored to rank identified subnetworks. We found that the majority of the top 10 ranked subnetworks were associated with cancer pathways such as the MAPK signalling pathway. With Support Vector Machine (SVM) as a classifier based on the Area Under Curve (AUC) of the Receiver Operating Characteristic (ROC) curve using 10-fold cross-validation and cross-dataset validation, we showed that gene subnetwork biomarkers obtained by RNA-seq-based GSNFS analysis had excellent classification performance. Additionally, when comparing the top-ranked subnetworks obtained from RNA-seq-based GSNFS analysis with those top-ranked subnetworks previously obtained from DNA microarray-based GSNFS analysis, we could categorize subnetworks and found unique pathways of cancer for each data-based analysis. © 2020 ACM.
คำสำคัญ
Feature Selection, lung cancer, RNA-Seq
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