Functional evaluation and analysis of predicted miRNA-mRNA regulatory network
| dc.contributor.advisor | Ruan, Jianhua | |
| dc.contributor.author | Tamim, Saleh | |
| dc.contributor.committeeMember | Robbins, Kay | |
| dc.contributor.committeeMember | Zhang, Weining | |
| dc.date.accessioned | 2024-03-08T15:44:17Z | |
| dc.date.available | 2024-03-08T15:44:17Z | |
| dc.date.issued | 2012 | |
| dc.description | This item is available only to currently enrolled UTSA students, faculty or staff. To download, navigate to Log In in the top right-hand corner of this screen, then select Log in with my UTSA ID. | |
| dc.description.abstract | MicroRNAs (miRNAs) are known to play important roles in the way their target genes behave. By causing translational repression or degradation of their targets, miRNAs affect many biological functions and in turn are linked to many diseases. One way of understanding the functions of miRNAs is by identifying and analyzing their target genes through lab experiments. However, due to the inefficiency of this method, alternative ways had to be explored, one of which is the use of computational and bioinformatics approaches. Even though these approaches can save a lot of experimental time and chemical cost, their results can be questionable. In this study, we evaluated three commonly used target prediction tools (miRanda, PITA, and TargetScan) by analyzing their predicted targets through miRNA-mRNA network. Our approach combines the use of function similarity between co-regulated targets, gene co-expression, and functional enrichment in determining the most probable true targets. Our results show widespread of predicted targets with little overlap among the tools. Overall, TargetScan seems to perform better compared to the other two. | |
| dc.description.department | Computer Science | |
| dc.format.extent | 45 pages | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.isbn | 9781267616036 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12588/5696 | |
| dc.language | en | |
| dc.subject.classification | Computer science | |
| dc.title | Functional evaluation and analysis of predicted miRNA-mRNA regulatory network | |
| dc.type | Thesis | |
| dc.type.dcmi | Text | |
| dcterms.accessRights | pq_closed | |
| thesis.degree.department | Computer Science | |
| thesis.degree.grantor | University of Texas at San Antonio | |
| thesis.degree.level | Masters | |
| thesis.degree.name | Master of Science |
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