Abstract
It is well known that numerous long noncoding RNAs (lncRNAs) closely relate to the physiological and pathological processes of human diseases and can serves as potential biomarkers. Therefore, lncRNA-disease associations that are identified by computational methods as the targeted candidates reduce the cost of biological experiments focusing on deep study furtherly. However, inaccurate construction of similarity networks and inadequate numbers of observed known lncRNA–disease associations, such inherent problems make many mature computational methods that have been developed for many years still exit some limitations. It motivates us to explore a new computational method that was fused with KATZ measure and space projection to fast probing potential lncRNA-disease associations (namely KATZSP). KATZSP is comprised of following key steps: combining all the global information with which to change Boolean network of known lncRNA–disease associations into the weighted networks; changing the similarities calculation into counting the number of walks that connect lncRNA nodes and disease nodes in bipartite graphs; obtaining the space projection scores to refine the primary prediction scores. The process to fuse KATZ measure and space projection was simplified and uncomplicated with needing only one attenuation factor. The leave-one-out cross validation (LOOCV) experimental results showed that, compared with other state-of-the-art methods (NCPLDA, LDAI-ISPS and IIRWR), KATZSP had a higher predictive accuracy shown with area-under-the-curve (AUC) value on the three datasets built, while KATZSP well worked on inferring potential associations related to new lncRNAs (or isolated diseases). The results from real cases study (such as pancreas cancer, lung cancer and colorectal cancer) further confirmed that KATZSP is capable of superior predictive ability to be applied as a guide for traditional biological experiments.
Highlights
Long non-coding RNAs whose length are longer than 200 nucleotides have crucial roles in gene expression control during developmental and differentiational processes [1]
With strong data support from long noncoding RNAs (lncRNAs) related databases and similarity calculation based on miRNA information [15,16,17,18,19,20], the computational prediction models that were built to infer lncRNA– disease associations could supply more accurate targeted candidates [21]: 1) saving cost and time for biological experiments; 2) making bio-experiments focus on deeper study of targets; 3) speeding up understanding the pathogenesis of complex diseases
3) Convolutional neural network (CNN) based inferring models [40,41,42,43], are at the early research stage, with consuming relatively high time complexity and relying on the quality of multiple sources biological data as well. Those above models still have different limitations, such as, needing negative samples, not being able to infer associations related to isolated diseases and new lncRNAs directly, not high accuracy with singular methodology. Addressing these limitations, we explored a novel prediction method based on the fusion of KATZ Measure and Space Projection to infer potential lncRNA-disease associations in bipartite graphs, namely KATZ first and then space projection (KATZSP)
Summary
Long non-coding RNAs (lncRNAs) whose length are longer than 200 nucleotides (nt) have crucial roles in gene expression control during developmental and differentiational processes [1]. 3) Convolutional neural network (CNN) based inferring models [40,41,42,43], are at the early research stage, with consuming relatively high time complexity and relying on the quality of multiple sources biological data as well Those above models still have different limitations, such as, needing negative samples, not being able to infer associations related to isolated diseases and new lncRNAs directly, not high accuracy with singular methodology. Addressing these limitations, we explored a novel prediction method based on the fusion of KATZ Measure and Space Projection to infer potential lncRNA-disease associations in bipartite graphs, namely KATZSP
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