Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3122
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
In recent years, the role of miRNAs in post-transcriptional gene regulation has provided new insights into the understanding of several types of cancers and neurological disorders. Although miRNA research has gathered great momentum since its discovery, traditional biological methods for finding miRNA genes and targets continue to remain a huge challenge due to the laborious tasks and extensive time involved. Fortunately, advances in computational methods have yielded considerable improvements in miRNA studies. This literature review briefly discusses recent machine learning-based techniques applied in the discovery of miRNAs, prediction of miRNA targets, and inference of miRNA functions. We also discuss the limitations of how these approaches have been elucidated in previous studies.
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Source |
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http://dx.doi.org/10.1007/978-1-4939-7046-9_16 | DOI Listing |
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