Evolution-informed modeling improves outcome prediction for cancers

Simple item page
dc.citation.journalTitleEvolutionary Applications
dc.contributor.authorLiu, Li
dc.contributor.authorChang, Yung
dc.contributor.authorYang, Tao
dc.contributor.authorNoren, David P.
dc.contributor.authorLong, Byron
dc.contributor.authorKornblau, Steven
dc.contributor.authorQutub, Amina
dc.contributor.authorYe, Jieping
dc.date.accessioned2016-12-16T17:50:26Z
dc.date.available2016-12-16T17:50:26Z
dc.date.issued2016
dc.description.abstractDespite wide applications of high-throughput biotechnologies in cancer research, many biomarkers discovered by exploring large-scale omics data do not provide satisfactory performance when used to predict cancer treatment outcomes. This problem is partly due to the overlooking of functional implications of molecular markers. Here, we present a novel computational method that uses evolutionary conservation as prior knowledge to discover bona fide biomarkers. Evolutionary selection at the molecular level is nature's test on functional consequences of genetic elements. By prioritizing genes that show significant statistical association and high functional impact, our new method reduces the chances of including spurious markers in the predictive model. When applied to predicting therapeutic responses for patients with acute myeloid leukemia and to predicting metastasis for patients with prostate cancers, the new method gave rise to evolution-informed models that enjoyed low complexity and high accuracy. The identified genetic markers also have significant implications in tumor progression and embrace potential drug targets. Because evolutionary conservation can be estimated as a gene-specific, position-specific, or allele-specific parameter on the nucleotide level and on the protein level, this new method can be extended to apply to miscellaneous ﾓomicsﾔ data to accelerate biomarker discoveries.
dc.identifier.citationLiu, Li, Chang, Yung, Yang, Tao, et al.. "Evolution-informed modeling improves outcome prediction for cancers." <i>Evolutionary Applications,</i> (2016) Wiley: http://dx.doi.org/10.1111/eva.12417.
dc.identifier.doihttp://dx.doi.org/10.1111/eva.12417
dc.identifier.urihttps://hdl.handle.net/1911/93725
dc.language.isoeng
dc.publisherWiley
dc.rightsThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleEvolution-informed modeling improves outcome prediction for cancers
dc.typeJournal article
dc.type.dcmiText
dc.type.publicationpublisher version
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Liu_et_al-2016.pdf
Size:
1.5 MB
Format:
Adobe Portable Document Format
Download
Collections
Faculty Publications
Bioengineering Publications