| dc.contributor.author | Zaman, Eshita | |
| dc.description.abstract | Diseases can be grouped according to phenotypic and genotypic similarities. Gene expression and micro-RNA data paved the way to look inside the genetic coding and classify diseases accurately. Modern system biology seeks to understand the underlying protein complexes in a cell and how they are altered in disease condition. In this research, we aimed to mine cohesive biological modules from large micro-RNA dataset and show the genes in these modules are dysregulated in a number of diseases. We used 13 different types of cancer and DME algorithm to extract dense modules satisfying a user defined density. Binary attribute proles of genes are also provided. We have shown that disease similarity based on the average module dysregulation yield disease pairs that share common disease genes. Collectively, we have concluded that the recurrence of these modules in different cancer types increase the therapeutic opportunity to treat more diseases with existing drugs. | en_US |
| dc.publisher | North Dakota State University | en_US |
| dc.rights | NDSU Policy 190.6.2 | |
| dc.title | Disease Similarity Using Biological Module Dysregulation Profile | en_US |
| dc.type | Master's paper | en_US |
| dc.date.accessioned | 2016-12-21T21:13:34Z | |
| dc.date.available | 2016-12-21T21:13:34Z | |
| dc.date.issued | 2016 | |
| dc.identifier.uri | http://hdl.handle.net/10365/25887 | |
| dc.subject.lcsh | Data mining. | en_US |
| dc.subject.lcsh | Bioinformatics. | en_US |
| dc.subject.lcsh | Genomics. | en_US |
| dc.subject.lcsh | Cluster analysis. | en_US |
| dc.subject.lcsh | Computer algorithms. | en_US |
| dc.rights.uri | https://www.ndsu.edu/fileadmin/policy/190.pdf | |
| ndsu.degree | Master of Science (MS) | en_US |
| ndsu.college | Engineering | en_US |
| ndsu.department | Computer Science | en_US |
| ndsu.program | Computer Science | en_US |
| ndsu.advisor | Salem, Saeed | |
