Title:  mirPLS: a partial linear structure identifier method for disease subtyping using MicroRNAs

Speaker:   Hua Liang  (Professor)

Time:  June 11,  2026, 10:00-11:00

Venue: Room 4318, Building No. 4, Wushan Campus

Abstract: 

MicroRNAs (miRNAs) are small non-coding RNAs that have been successfully identified to be differentially expressed in various diseases. However, some miRNAs were reported to be up-regulated in one subtype of a disease but down-regulated in another, making overall associations between these miRNAs and the heterogeneous disease non-linear. These non-linearly associated miRNAs, if identified, are thus informative biomarkers for disease subtyping. Here, we propose mirPLS, a Partial Linear Structure identifier for miRNA data that simultaneously identifies miRNAs of linear or non-linear associations with diseases, when non-linearly associated miRNAs can then be used for subsequent disease 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 subsequently improves the disease 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.