Robust Inductive Matrix Completion Strategy to Explore Associations Between LincRNAs and Human Disease Phenotypes

Over the past few years, it has been established that a number of long intergenic non-coding RNAs (lincRNAs) are linked to a wide variety of human diseases. The relationship among many other lincRNAs still remains as puzzle. Validation of such link between the two entities through biological experim...

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Veröffentlicht in:IEEE/ACM transactions on computational biology and bioinformatics Jg. 16; H. 6; S. 2066 - 2077
Hauptverfasser: Biswas, Ashis Kumer, Kim, Dong-Chul, Kang, Mingon, Gao, Jean X.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.11.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Bioinformatics
Data mining
Datasets
Disease
disease phenotypes
Diseases
Gene sequencing
Genomes
Genomics
heterogeneous feature integration
inductive learning
Information processing
Learning algorithms
lincRNA-disease association
Long intergenic non-coding RNA (LincRNA)
Machine learning
Next-generation sequencing
Outliers (statistics)
Phenotypes
Regularization
RNA
robust matrix completion
Robustness
ISSN:1545-5963, 1557-9964, 1557-9964
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Zusammenfassung:Over the past few years, it has been established that a number of long intergenic non-coding RNAs (lincRNAs) are linked to a wide variety of human diseases. The relationship among many other lincRNAs still remains as puzzle. Validation of such link between the two entities through biological experiments is expensive. However, piles of information about the two are becoming available, thanks to the High Throughput Sequencing (HTS) platforms, Genome Wide Association Studies (GWAS), etc., thereby opening opportunity for cutting-edge machine learning and data mining approaches. However, there are only a few in silico lincRNA-disease association inference tools available to date, and none of these utilizes side information of both the entities. The recently developed Inductive Matrix Completion (IMC) technique provides a recommendation platform among two entities considering respective side information. But, the formulation of IMC is incapable of handling noise and outliers that may present in the dataset, while data sparsity consideration is another issue with the standard IMC method. Thus, a robust version of IMC is needed that can solve these two issues. As a remedy, in this paper, we propose Robust Inductive Matrix Completion (RIMC) using 12;1 norm loss function aswell as 12;1 norm based regularization. We applied RIMC to the available association data between human lincRNAs and OMIM disease phenotypes as well as a diverse set of side information about the lincRNAs and the diseases. Our method performs better than the state-of-the-art methods in terms of precision@k and recall@k at the top-k disease prioritization to the subject lincRNAs. We also demonstrate that RIMC is equally effective forquerying about novel lincRNAs, as well as predicting rankof a newly known disease for a set of well-characterized lincRNAs. Availability: All the supporting datasets are available at the publicly accessible URL located at http://biomecis.uta.edu/~ashis/res/RIMC/.
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ISSN:1545-5963
1557-9964
1557-9964
DOI:10.1109/TCBB.2018.2844816