Year: 2018
Type: dissertation
Abstract: Cancer is a leading cause of death worldwide and a major public health burden. The rapid advancements in high-throughput techniques have now made it possible to molecularly characterize large number of patient tumors, and large-scale genomic and functional profiles are routinely being generated. Such datasets hold immense potential to reveal novel genes driving cancer, biomarkers with prognostic value, and also identify promising targets for drug treatment. But the ‘big data’ nature of these highly complex datasets require concurrent development of computational models and data analysis strategies to be able to mine useful knowledge and unlock the potential of the information content that is latent in such datasets. This thesis presents computational and analytical approaches to extract potentially useful information by integrating genomic and functional profiles of cancer cells.
Source: Työväentutkimus Vuosikirja
Author: Alok Jaiswal
Institution: University of Helsinki
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W2803135878