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Current day biology is rapidly progressing to become a data-intensive enterprise. The recent advancements in biological instrumentation have caused an explosion in the volume and depth of biological data, which in turn poses a dual challenge to biologists. First, they are confronted with need for the efficient storage and management of massive amounts of data. Second, the data needs to be analysed and interpreted to gain useful knowledge. Both these tasks are gaining vital significance in life science research. In this scenario, the apparently disparate disciplines of biology and computer science must embrace each other and strive to attain a common goal of optimal information management. In this context, this thesis attempts to apply computational principles and tech- niques to shed light on the unexplored properties and roles of biological processes and diseases.
Current day biology is rapidly progressing to become a data-intensive enterprise. The recent advancements in biological instrumentation have caused an explosion in the volume and depth of biological data, which in turn poses a dual challenge to biologists. First, they are confronted with need for the efficient storage and management of massive amounts of data. Second, the data needs to be analysed and interpreted to gain useful knowledge. Both these tasks are gaining vital significance in life science research. In this scenario, the apparently disparate disciplines of biology and computer science must embrace each other and strive to attain a common goal of optimal information management. In this context, this thesis attempts to apply computational principles and tech- niques to shed light on the unexplored properties and roles of biological processes and diseases.
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