Bulletin of the American Physical Society
APS March Meeting 2022
Volume 67, Number 3
Monday–Friday, March 14–18, 2022; Chicago
Session Q07: Mechanobiology of Cell-Medium Interactions I
3:00 PM–6:00 PM,
Wednesday, March 16, 2022
Room: McCormick Place W-179A
Sponsoring
Unit:
DBIO
Chair: C. Nadir Kaplan, Virginia Tech
Abstract: Q07.00004 : Multidimensional mechano-tomography of biological cells: novel modes and machine learning data analysis*
3:36 PM–4:12 PM
Presenter:
Igor Y Sokolov
(Tufts University)
Authors:
Igor Y Sokolov
(Tufts University)
Maxim Dokukin
(Tufts University, Sarov Physical and Technical Institute, and Nanoscience Solutions, Inc)
Renato Aguliera
(Tufts University)
Nadezda Makarova
(Tufts University)
This wealth of information can effectively be analyzed using a machine learning approach to define cell phenotype and its specific stage. “Classical” deep learning methods, however, are not directly applicable to AFM imaging because they require an unrealistically large number of images for training. This is technically impossible because AFM is a relatively slow technique to obtain the required number of images. I will describe our solution which is based on a substantial reduction of the dimension of the data space by using so-called surface parameters. An example of this approach will be described. The steps to produce sufficient statistics using bootstrap methods will be overviewed. An important problem of overtraining will be discussed. Examples of applications of this method will be given.
*NSF CMMI 1435655; NSF CMMI 1937373; NIH R01CA262147
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