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Machine Learning in Nano-Scale Biomedical Engineering

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Document pages: 31 pages

Abstract: Machine learning (ML) empowers biomedical systems with the capability tooptimize their performance through modeling of the available data extremelywell, without using strong assumptions about the modeled system. Especially innano-scale biosystems, where the generated data sets are too vast and complexto mentally parse without computational assist, ML is instrumental in analyzingand extracting new insights, accelerating material and structure discoveries,and designing experience as well as supporting nano-scale communications andnetworks. However, despite these efforts, the use of ML in nano-scalebiomedical engineering remains still under-explored in certain areas andresearch challenges are still open in fields such as structure and materialdesign and simulations, communications and signal processing, and bio-medicineapplications. In this article, we review the existing research regarding theuse of ML in nano-scale biomedical engineering. In more detail, we firstidentify and discuss the main challenges that can be formulated as ML problems.These challenges are classified into the three aforementioned main categories.Next, we discuss the state of the art ML methodologies that are used tocountermeasure the aforementioned challenges. For each of the presentedmethodologies, special emphasis is given to its principles, applications, andlimitations. Finally, we conclude the article with insightful discussions, thatreveal research gaps and highlight possible future research directions.

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