A #preprint of our #paper, entitled “Machine Learning in Nano-Scale Biomedical Engineering”, has been uploaded in #ArXiV. #MachineLearning (ML) empowers #biomedical systems with the capability to optimize their performance through modeling of the available data extremely well, without using strong assumptions about the modeled system. Especially in #nano-scale #biosystems, where the generated #datasets are too vast and complex to mentally parse without computational assist, ML is instrumental in analyzing and extracting new insights, accelerating #material and #structure discoveries, and designing experience as well as supporting nano-scale #communications and #networks. However, despite these efforts, the use of ML in nano-scale #biomedical #engineering remains still under-explored in certain areas and research challenges are still open in fields such as structure and material design and simulations, communications and signal processing, and bio-medicine applications. This paper is a review of the existing #research literature in this field. You can read the full paper at https://lnkd.in/gCNGSba.