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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | Ravie Chandren Muniyandi, Assoc. Prof. Dr. | |
| dc.contributor.author | Amirhossein Sahlabadi (P74166) | |
| dc.date.accessioned | 2023-10-06T09:19:22Z | - |
| dc.date.available | 2023-10-06T09:19:22Z | - |
| dc.date.issued | 2017-07 | |
| dc.identifier.other | ukmvital:107079 | |
| dc.identifier.uri | https://ptsldigital.ukm.my/jspui/handle/123456789/476486 | - |
| dc.description | Nowadays, microarray technology has become one of the most popular way to study genes expression and diagnosis of diseases. National institutions including National Centre for Biology Information (NCBI) which hosts wide variety of public databases such as MedBank and PubMed house large volume of biological data. These data need to be preprocessed as they contain high level of noises and biases. Robust Multi-Array Average (RMA) is one of the standard and popular method to preprocess the data and remove the noises. Most of the preprocessing algorithms are time-consuming and unable to handle large amount of dataset with tens to thousands of experiments. Parallel processing can be used to address the aforementioned issues. Hadoop is a well-known and ideal distributed system file framework that provide parallel environment to run the experiment. In this thesis, the capability of Hadoop and statistical power of R have been leveraged to parallelise the available preprocessing algorithm called RMA to efficiently process microarray data. The experiment has been run on multi-core CPU, where the efficiency as well as the performance of parallelised RMA algorithm has been compared with traditional sequential RMA method. This experiment has been carried out on Intel core i3 DELL Inspiron15 with 8 gigabyte RAM on Ubuntu 16.04. The result shows the speed-up rate for parallel approach can be close to 2 times faster than the sequential one in some instances.,“Certification of Master's/Doctoral Thesis” is not available,Master of Computer Science | |
| dc.language.iso | eng | |
| dc.publisher | UKM, Bangi | |
| dc.relation | Faculty of Information Science and Technology / Fakulti Teknologi dan Sains Maklumat | |
| dc.rights | UKM | |
| dc.subject | Array processors | |
| dc.subject | Universiti Kebangsaan Malaysia -- Dissertations | |
| dc.subject | Dissertations, Academic -- Malaysia | |
| dc.title | Parallelised robust multi-array average for preprocessing of microarray data using hadoop | |
| dc.type | theses | |
| dc.format.pages | 78 | |
| dc.identifier.callno | QA76.5.S234 2017 3 tesis | |
| dc.identifier.barcode | 003805(2019) | |
| Appears in Collections: | Faculty of Information Science and Technology / Fakulti Teknologi dan Sains Maklumat | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| ukmvital_107079+SOURCE1+SOURCE1.0.PDF Restricted Access | 1.2 MB | Adobe PDF |  View/Open |
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