English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

MicroRNAs (miRNAs) play crucial roles in multifarious biological processesassociated with human diseases. Identifying potential miRNA-diseaseassociations contributes to understanding the molecular mechanisms ofmiRNA-related diseases. Most of the existing computational methods mainly focuson predicting whether a miRNA-disease association exists or not. However, theroles of miRNAs in diseases are prominently diverged, for instance, Geneticvariants of microRNA (mir-15) may affect expression level of miRNAs leading toB cell chronic lymphocytic leukemia, while circulating miRNAs (includingmir-1246, mir-1307-3p, etc.) have potentials to detecting breast cancer in theearly stage. In this paper, we aim to predict multi-type miRNA-diseaseassociations instead of taking them as binary. To this end, we innovativelyrepresent miRNA-disease-type triplets as a tensor and introduce TensorDecomposition methods to solve the prediction task. Experimental results on twowidely-adopted miRNA-disease datasets: HMDD v2.0 and HMDD v3.2 show that tensordecomposition methods improve a recent baseline in a large scale (up to 38% intop-1 F1). We further propose a novel method, Tensor Decomposition withRelational Constraints (TDRC), which incorporates biological features asrelational constraints to further the existing tensor decomposition methods.Compared with two existing tensor decomposition methods, TDRC can producebetter performance while being more efficient.

Tensor Decomposition with Relational Constraints for Predicting Multiple Types of MicroRNA-disease Associations