Clustering-based gene-subnetwork biomarker identification using gene expression data

Conference proceedings article

ผู้เขียน/บรรณาธิการ

กลุ่มสาขาการวิจัยเชิงกลยุทธ์

ไม่พบข้อมูลที่เกี่ยวข้อง

รายละเอียดสำหรับงานพิมพ์

รายชื่อผู้แต่ง: Doungpan N., Engchuan W., Meechai A., Chan J.H.

ผู้เผยแพร่: Hindawi

ปีที่เผยแพร่ (ค.ศ.): 2015

Volume number: 2015-September

ISBN: 9781479919604; 9781479919604; 9781479919604; 9781479919604

นอก: 0146-9428

eISSN: 1745-4557

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84951135358&doi=10.1109%2fIJCNN.2015.7280786&partnerID=40&md5=6371665cf46f51a6c0354186012112e1

ภาษา: English-Great Britain (EN-GB)

ดูบนเว็บไซต์ของสำนักพิมพ์

บทคัดย่อ

The identification of predictive biomarkers of complex disease with robustness and specificity is an ongoing challenge. Gene expressions provide information on how the cell reacts to a particular state and the relationship of genes may lead to novel information. A network-based approach integrating expression data with protein-protein interaction network can be used to identify gene-subnetwork biomarkers for a particular disease. However, cancer datasets are heterogeneous in nature containing unknown or undefined subtypes of cancers. In this study, we propose a gene-subnetwork biomarker identification approach by implementing an Expectation-Maximization (EM) clustering technique to homogenize the dataset. To validate our proposed method. Lung cancer expression datasets are used to identify gene-subnetwork biomarkers. The evaluation of gene-subnetwork biomarkers is done by 5-fold cross-validation on an independent dataset. The comparison between non-clustering and clustering-based gene-subnetwork identification showed that clustering produced improved classification performance at a statistically significant level. Furthermore, preliminary functional analysis results showed more significant subnetworks were identified using the proposed approach. ฉ 2015 IEEE.

คำสำคัญ

gene-subnetwork