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
Online-Zugang:	Volltext
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