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A semantics-oriented computational approach to investigate microRNA regulation on glucocorticoid resistance in pediatric acute lymphoblastic leukemia

Overview of attention for article published in BMC Medical Informatics and Decision Making, July 2018
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Title
A semantics-oriented computational approach to investigate microRNA regulation on glucocorticoid resistance in pediatric acute lymphoblastic leukemia
Published in
BMC Medical Informatics and Decision Making, July 2018
DOI 10.1186/s12911-018-0637-3
Pubmed ID
Authors

Huiqin Chen, Dihua Zhang, Guoping Zhang, Xiaofeng Li, Ying Liang, Mohan Vamsi Kasukurthi, Shengyu Li, Glen M. Borchert, Jingshan Huang

Abstract

Acute lymphoblastic leukemia is the most prevalent neoplasia among children. Despite the tremendous achievements of state-of-the-art treatment strategies, drug resistance is still a major cause of chemotherapy failure leading to relapse in pediatric acute lymphoblastic leukemia. The underlying mechanisms of such phenomenon are not yet clear and subject to further exploration. Prior research has shown that microRNAs can act as post-transcriptional regulators of many genes related to drug resistance. However, details of microRNA regulation mechanisms in pediatric acute lymphoblastic leukemia are far from completely understood. We utilized a computational approach based upon emerging biomedical and biological ontologies and semantic technologies to investigate the important roles of microRNA: mRNA regulation on glucocorticoid resistance in pediatric acute lymphoblastic leukemia. In particular, various filtering mechanisms were designed based on the user-provided MeSH term to narrow down the most promising microRNAs in an effective manner. During our manual search on background literature, we found a total of 18 candidate microRNAs that possibly regulate glucocorticoid resistance in pediatric acute lymphoblastic leukemia. After the first-round filtering using the Broader-Match option where both the user-provided MeSH term and its direct parent term were utilized, the number of targets for 18 microRNAs was reduced from 232 to 74. During the second-round filtering with the Exact-Match option where only the MeSH term itself was utilized, the number of targets was further reduced to 19. Finally, we conducted semantic searches in the OmniSearch software tool on the five likely regulating microRNAs and identified two most likely microRNAs. We successfully identified two microRNAs, hsa-miR-142-3p and hsa-miR-17-5p, which are computationally predicted to closely relate to glucocorticoid resistance, thus potentially serving as novel biomarkers and therapeutic targets in pediatric acute lymphoblastic leukemia.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 32 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 5 16%
Student > Master 4 13%
Student > Ph. D. Student 3 9%
Researcher 2 6%
Professor > Associate Professor 2 6%
Other 4 13%
Unknown 12 38%
Readers by discipline Count As %
Medicine and Dentistry 6 19%
Biochemistry, Genetics and Molecular Biology 3 9%
Computer Science 3 9%
Agricultural and Biological Sciences 1 3%
Immunology and Microbiology 1 3%
Other 3 9%
Unknown 15 47%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 August 2018.
All research outputs
#20,529,173
of 23,098,660 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,824
of 2,013 outputs
Outputs of similar age
#288,124
of 329,730 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#23
of 27 outputs
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So far Altmetric has tracked 2,013 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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