Study on Integrative Clustering of Multiple Genomic Data to Discover Cancer Subtypes.
Date of Submission
December 2014
Date of Award
Winter 12-12-2015
Institute Name (Publisher)
Indian Statistical Institute
Document Type
Master's Dissertation
Degree Name
Master of Technology
Subject Name
Computer Science
Department
Machine Intelligence Unit (MIU-Kolkata)
Supervisor
Maji, Pradipta (MIU-Kolkata; ISI)
Abstract (Summary of the Work)
With the advancement of technology, different sources of genetic information become available with a low cost. In the research for finding cancer subtypes, what will help to proceed with a targeted treatment, this opened up a new dimension. However, the basic problem is how to reach towards a proper integration scheme such that both the personal significance and interactive information is conserved, because only then it will be possible to utilize the data resource and obtain richer information about subtypes. On the other hand, as the subtypes are not always properly defined or even known, thus any solution should be unsupervised in nature. This study presented an integration scheme based on the concept of iCluster method, to address these issues. With its many merits, however the crisp nature of clusters obtained by iCluster is not always natural in the case of overlapping and incomplete nature of the data, thus a rough-fuzzy clustering approach will be more suitable, where an addition of intelligent initial center selection algorithm is most desired. A variety of cluster validation index are used to support the claims and present the findings on two different cancer data.
Control Number
ISI-DISS-2014-332
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
DOI
http://dspace.isical.ac.in:8080/jspui/handle/10263/6489
Recommended Citation
Mullick, Sankha Subhra, "Study on Integrative Clustering of Multiple Genomic Data to Discover Cancer Subtypes." (2015). Master’s Dissertations. 387.
https://digitalcommons.isical.ac.in/masters-dissertations/387
Comments
ProQuest Collection ID: http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:28843741