Network-based method for inferring cancer progression at the pathway level from cross-sectional mutation data

aut.researcherKasabov, Nikola
dc.contributor.authorWu, Hen_NZ
dc.contributor.authorGao, Len_NZ
dc.contributor.authorKasabov, Nen_NZ
dc.date.accessioned2016-04-03T22:01:27Z
dc.date.available2016-04-03T22:01:27Z
dc.date.copyright2016-02-19en_NZ
dc.date.issued2016-02-19en_NZ
dc.description.abstractLarge-scale cancer genomics projects are providing a wealth of somatic mutation data from a large number of cancer patients. However, it is difficult to obtain several samples with a temporal order from one patient in evaluating the cancer progression. Therefore, one of the most challenging problems arising from the data is to infer the temporal order of mutations across many patients. To solve the problem efficiently, we present a Network-based method (NetInf) to Infer cancer progression at the pathway level from cross-sectional data across many patients, leveraging on the exclusive property of driver mutations within a pathway and the property of linear progression between pathways. To assess the robustness of NetInf, we apply it on simulated data with the addition of different levels of noise. To verify the performance of NetInf, we apply it to analyze somatic mutation data from three real cancer studies with large number of samples. Experimental results reveal that the pathways detected by NetInf show significant enrichment. Our method reduces computational complexity by constructing gene networks without assigning the number of pathways, which also provides new insights on the temporal order of somatic mutations at the pathway level rather than at the gene level.en_NZ
dc.identifier.citationIEEE/ACM Transactions on Computational Biology and Bioinformatics (Volume:PP , Issue: 99 ). DOI: 10.1109/TCBB.2016.2520934en_NZ
dc.identifier.doi10.1109/TCBB.2016.2520934en_NZ
dc.identifier.issn1557-9964en_NZ
dc.identifier.urihttps://hdl.handle.net/10292/9654
dc.languageENGen_NZ
dc.publisherIEEE
dc.relation.urihttp://dx.doi.org/10.1109/TCBB.2016.2520934
dc.rightsCopyright © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.rights.accessrightsOpenAccessen_NZ
dc.subjectCancer genome; Cancer progression; Driver mutation; Driver pathways; Complex network
dc.titleNetwork-based method for inferring cancer progression at the pathway level from cross-sectional mutation dataen_NZ
dc.typeJournal Article
pubs.elements-id200352
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Design & Creative Technologies
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