We apply and enhance the cornerstone theoretical fundamentals of engineering bioinformatics to complement nanotechnology. In particular, nanoengineering bioinformatics is examined and formulated as a coherent abstraction in cognitive analysis of complex inorganic, organic and hybrid nanosystems. We report the application of an entropy-enhanced frequency-domain analysis concept to examine large-scale genomic data. This ensures superior coherency for qualitative and quantitative analysis. Conventionally, bioinformatics emphasizes the application of statistical methods attempting to analyze large-scale data produced by high-throughput experiments including complex gene sequencing. It is illustrated that bioinformatics can be expanded to a systems-based perspective by making use of novel concepts thereby positioning bioinformatics to play a significant role in engineering and technology. It is our goal to evolve the nanoengineering bioinformatics to coherently analyze the genomic data identifying, qualifying and quantifying complex genes in functional biological systems. The ultimate goal for the application of nanoengineering bioinformatics is in the development of system-level knowledge in order to devise novel paradigms for discovering entirely new systems with superior functionality and performance. In contrast, biomedical informatics examines the data from a more narrow-focused perspective and focuses on data and knowledge integration to analyze the biological processes. To enable the integration between computational, experimental, stochastic and deterministic modeling, novel information-theoretical methods should be applied to guarantee coherent representation and possible evaluation from organic to hybrid systems. These methods must be robust and utilize incomplete and inaccurate information, sequencing gaps, noncoding regions, unsolved interactions, multiple modeling hierarchies, unknown phenomena, lack of information, etc. It is demonstrated that the proposed entropy-enhanced frequency-domain concept promises to solve a number of long-standing problems. The reported paradigm complements a number of far-reaching perceptions of engineering bioinformatics.

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Copyright 2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. ISBN: 0-7803-8335-4Note: imported from RIT’s Digital Media Library running on DSpace to RIT Scholar Works in February 2014.

Document Type

Conference Proceeding

Department, Program, or Center

Microelectronic Engineering (KGCOE)


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