Efficient network based approaches for pattern recognition and knowledge discovery from large and heterogeneous datasets
Year: 2013
Type: article
Abstract: With rapid technological advances, the potential for transformational science and engineering for all scientific domains is enormous.Discovering useful and meaningful patterns and knowledge extraction from large, diverse, distributed and heterogeneous datasets however continues to pose a formidable challenge.Thus, there is an urgent need for more efficient and robust computational approaches to effectively manage, use, and exploit these heterogeneous data sources.This in turn can accelerate the progress of scientific discovery and innovation; gain new insights in a timely manner; lead to new fields of inquiry hitherto impossible.In this dissertation, we tackle this challenge by developing and applying novel and efficient network-based computational approaches.To demonstrate the utility of our algorithms, we use several large and heterogeneous datasets from biomedical domain, I would like to express my acknowledgement to advisors Dr. Anil Jegga and Dr.
Author: Cheng Zhu
Institution: University of Cincinnati
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W2281075444