TY - JOUR AU - Müller, Paul AU - Abuhattum, Shada AU - Möllmert, Stephanie AU - Ulbricht, Elke AU - Taubenberger, Anna V. AU - Guck, Jochen PY - 2019 DA - 2019/09/10 TI - nanite: using machine learning to assess the quality of atomic force microscopy-enabled nano-indentation data JO - BMC Bioinformatics SP - 465 VL - 20 IS - 1 AB - Atomic force microscopy (AFM) allows the mechanical characterization of single cells and live tissue by quantifying force-distance (FD) data in nano-indentation experiments. One of the main problems when dealing with biological tissue is the fact that the measured FD curves can be disturbed. These disturbances are caused, for instance, by passive cell movement, adhesive forces between the AFM probe and the cell, or insufficient attachment of the tissue to the supporting cover slide. In practice, the resulting artifacts are easily spotted by an experimenter who then manually sorts out curves before proceeding with data evaluation. However, this manual sorting step becomes increasingly cumbersome for studies that involve numerous measurements or for quantitative imaging based on FD maps. SN - 1471-2105 UR - https://doi.org/10.1186/s12859-019-3010-3 DO - 10.1186/s12859-019-3010-3 ID - Müller2019 ER -