About: Principles and innovative technologies for decrypting noncoding RNAs: from discovery and functional prediction to clinical application

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Principles and innovative technologies for decrypting noncoding RNAs: from discovery and functional prediction to clinical application
Creator	Chen, Yue-Qin Sun, Yu-Meng
Source	Medline; PMC
abstract	Noncoding RNAs (ncRNAs) are a large segment of the transcriptome that do not have apparent protein-coding roles, but they have been verified to play important roles in diverse biological processes, including disease pathogenesis. With the development of innovative technologies, an increasing number of novel ncRNAs have been uncovered; information about their prominent tissue-specific expression patterns, various interaction networks, and subcellular locations will undoubtedly enhance our understanding of their potential functions. Here, we summarized the principles and innovative methods for identifications of novel ncRNAs that have potential functional roles in cancer biology. Moreover, this review also provides alternative ncRNA databases based on high-throughput sequencing or experimental validation, and it briefly describes the current strategy for the clinical translation of cancer-associated ncRNAs to be used in diagnosis.
has issue date	2020-08-10(xsd:dateTime)
bibo:doi	10.1186/s13045-020-00945-8
bibo:pmid	32778133
has license	cc-by
sha1sum (hex)	056c139f583f80099a4034c4677ea3923b6db9b9
schema:url	https://doi.org/10.1186/s13045-020-00945-8
resource representing a document's title	Principles and innovative technologies for decrypting noncoding RNAs: from discovery and functional prediction to clinical application
has PubMed Central identifier	PMC7416809
has PubMed identifier	32778133
schema:publication	J Hematol Oncol
resource representing a document's body	covid:056c139f583f80099a4034c4677ea3923b6db9b9#body_text
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