Name
Title | ||
|---|---|---|
Mirpls: A Partial Linear Structure Identifier Method For Cancer Subtyping Using Micrornas |
Abstract | ||
|---|---|---|
Motivation: MicroRNAs (miRNAs) are small non-coding RNAs that have been successfully identified to be differentially expressed in various cancers. However, some miRNAs were reported to be up-regulated in one subtype of a cancer but down-regulated in another, making overall associations between these miRNAs and the heterogeneous cancer non-linear. These non-linearly associated miRNAs, if identified, are thus informative for cancer subtyping.Results: Here, we propose mirPLS, a Partial Linear Structure identifier for miRNA data that simultaneously identifies miRNAs of linear or non-linear associations with cancer status when non-linearly associated miRNAs can then be used for subsequent cancer subtyping. Simulation studies showed that mirPLS can identify both non-linearly and linearly outcome-associated miRNAs more accurately than the comparison methods. Using the identified non-linearly associated miRNAs much improves the cancer subtyping accuracy. Applications to miRNA data of three different cancer types suggest that the cancer subtypes defined by the non-linearly associated miRNAs identified by mirPLS are consistently more predictive of patient survival and more biological meaningful. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1093/bioinformatics/btaa606 | BIOINFORMATICS |
DocType | Volume | Issue |
Journal | 36 | 19 |
ISSN | Citations | PageRank |
1367-4803 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peifeng Ruan | 1 | 0 | 1.01 |
| Shuang Wang | 2 | 32 | 6.49 |
| Hua Liang | 3 | 14 | 7.25 |