Prioritizing human cancer microRNAs based on genes’ functional consistency between microRNA and cancer

Li, Xia; Wang, Qianghu; Zheng, Yan; Lv, Sali; Ning, Shangwei; Sun, Jie; Huang, Teng; Zheng, Qi-Fan; Ren, Huan; Xu, Jin; Wang, Xishan; Li, Yixue

doi:10.1093/nar/gkr770

Cited by 61 publications

(43 citation statements)

References 77 publications

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“…It is widely believed that computational models could yield the most potential miRNAs related to human diseases and are a valuable complementary tool for experimental methods. 28,32,[71][72][73] To more accurately predict potential miRNA-disease associations, we presented a computational model named KFRLSMDA involving diverse data sets: miRNA functional similarity, disease semantic similarity, miRNA-disease associations and Gaussian interaction profile kernel similarity for miRNAs and diseases. We first applied kernel fusion technique to fuse similarity matrices for miRNA and disease, and then utilized regularized least square algorithm to predict the final result based on two fused matrices.…”

Section: Discussionmentioning

confidence: 99%

In silico prediction of potential miRNA‐disease association using an integrative bioinformatics approach based on kernel fusion

Guan

Wang

Zhang

et al. 2019

J Cellular Molecular Medi

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Accumulating experimental evidence has demonstrated that microRNAs (miRNAs) have a huge impact on numerous critical biological processes and they are associated with different complex human diseases. Nevertheless, the task to predict potential miRNAs related to diseases remains difficult. In this paper, we developed a Kernel Fusion‐based Regularized Least Squares for MiRNA‐Disease Association prediction model (KFRLSMDA), which applied kernel fusion technique to fuse similarity matrices and then utilized regularized least squares to predict potential miRNA‐disease associations. To prove the effectiveness of KFRLSMDA, we adopted leave‐one‐out cross‐validation (LOOCV) and 5‐fold cross‐validation and then compared KFRLSMDA with 10 previous computational models (MaxFlow, MiRAI, MIDP, RKNNMDA, MCMDA, HGIMDA, RLSMDA, HDMP, WBSMDA and RWRMDA). Outperforming other models, KFRLSMDA achieved AUCs of 0.9246 in global LOOCV, 0.8243 in local LOOCV and average AUC of 0.9175 ± 0.0008 in 5‐fold cross‐validation. In addition, respectively, 96%, 100% and 90% of the top 50 potential miRNAs for breast neoplasms, colon neoplasms and oesophageal neoplasms were confirmed by experimental discoveries. We also predicted potential miRNAs related to hepatocellular cancer by removing all known related miRNAs of this cancer and 98% of the top 50 potential miRNAs were verified. Furthermore, we predicted potential miRNAs related to lymphoma using the data set in the old version of the HMDD database and 80% of the top 50 potential miRNAs were confirmed. Therefore, it can be concluded that KFRLSMDA has reliable prediction performance.

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Section: Discussionmentioning

confidence: 99%

In silico prediction of potential miRNA‐disease association using an integrative bioinformatics approach based on kernel fusion

Guan

Wang

Zhang

et al. 2019

J Cellular Molecular Medi

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“…In our previous study, we performed a framework to prioritize cancer risk miRNAs in a similar way used Gene Ontology data only [37]. Although achieved remarkable success, it overlooked the contribution from other functional data sets for studying gene sets association.…”

Section: Discussionmentioning

confidence: 99%

“…In previous work, we have prioritized human cancer miRNAs based on genes’ functional consistency [37]. In this study, we developed a miRNA target prioritization method named mirTarPri that used functional genomics data to rank predicted target lists.…”

Section: Introductionmentioning

confidence: 99%

mirTarPri: Improved Prioritization of MicroRNA Targets through Incorporation of Functional Genomics Data

et al. 2013

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MicroRNAs (miRNAs) are a class of small (19–25 nt) non-coding RNAs. This important class of gene regulator downregulates gene expression through sequence-specific binding to the 3′untranslated regions (3′UTRs) of target mRNAs. Several computational target prediction approaches have been developed for predicting miRNA targets. However, the predicted target lists often have high false positive rates. To construct a workable target list for subsequent experimental studies, we need novel approaches to properly rank the candidate targets from traditional methods. We performed a systematic analysis of experimentally validated miRNA targets using functional genomics data, and found significant functional associations between genes that were targeted by the same miRNA. Based on this finding, we developed a miRNA target prioritization method named mirTarPri to rank the predicted target lists from commonly used target prediction methods. Leave-one-out cross validation has proved to be successful in identifying known targets, achieving an AUC score up to 0. 84. Validation in high-throughput data proved that mirTarPri was an unbiased method. Applying mirTarPri to prioritize results of six commonly used target prediction methods allowed us to find more positive targets at the top of the prioritized candidate list. In comparison with other methods, mirTarPri had an outstanding performance in gold standard and CLIP data. mirTarPri was a valuable method to improve the efficacy of current miRNA target prediction methods. We have also developed a web-based server for implementing mirTarPri method, which is freely accessible at http://bioinfo.hrbmu.edu.cn/mirTarPri.

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“…Assessing similarity is crucial to expanding knowledge, because it allows us to categorize objects into kinds. Similar objects tend to behave similarly, which supports inference, a crucial task to support many applications including identifying protein-protein interactions [ 1 ], suggesting candidate genes involved in diseases [ 2 ] and evaluating the functional coherence of gene sets [ 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

Semantic Similarity in the Gene Ontology

Pesquita

2016

Methods in Molecular Biology

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Gene Ontology-based semantic similarity (SS) allows the comparison of GO terms or entities annotated with GO terms, by leveraging on the ontology structure and properties and on annotation corpora. In the last decade the number and diversity of SS measures based on GO has grown considerably, and their application ranges from functional coherence evaluation, protein interaction prediction, and disease gene prioritization.Understanding how SS measures work, what issues can affect their performance and how they compare to each other in different evaluation settings is crucial to gain a comprehensive view of this area and choose the most appropriate approaches for a given application.In this chapter, we provide a guide to understanding and selecting SS measures for biomedical researchers. We present a straightforward categorization of SS measures and describe the main strategies they employ. We discuss the intrinsic and external issues that affect their performance, and how these can be addressed. We summarize comparative assessment studies, highlighting the top measures in different settings, and compare different implementation strategies and their use. Finally, we discuss some of the extant challenges and opportunities, namely the increased semantic complexity of GO and the need for fast and effi cient computation, pointing the way towards the future generation of SS measures.

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Prioritizing human cancer microRNAs based on genes’ functional consistency between microRNA and cancer

Cited by 61 publications

References 77 publications

In silico prediction of potential miRNA‐disease association using an integrative bioinformatics approach based on kernel fusion

In silico prediction of potential miRNA‐disease association using an integrative bioinformatics approach based on kernel fusion

mirTarPri: Improved Prioritization of MicroRNA Targets through Incorporation of Functional Genomics Data

Semantic Similarity in the Gene Ontology

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