Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/90079
Title: Supervised learning to predict sperm sorting by magnetophoresis
Authors: Shen, Xinhui
Marcos
Koh, James Boon Yong
Keywords: Magnetophoresis
Slender Body Theory
Engineering::Mechanical engineering
Issue Date: 2018
Source: Koh, J. B. Y., Shen, X., & Marcos (2018). Supervised learning to predict sperm sorting by magnetophoresis. Magnetochemistry, 4(3), 31-. doi:10.3390/magnetochemistry4030031
Series/Report no.: Magnetochemistry
Abstract: Machine learning is gaining popularity in the commercial world, but its benefits are yet to be well-utilised by many in the microfluidics community. There is immense potential in bridging the gap between applied engineering and artificial intelligence as well as statistics. We illustrate this by a case study investigating the sorting of sperm cells for assisted reproduction. Slender body theory (SBT) is applied to compute the behavior of sperm subjected to magnetophoresis, with due consideration given to statistical variations. By performing computations on a small subset of the generated data, we train an ensemble of four supervised learning algorithms and use it to make predictions on the velocity of each sperm. Our results suggest that magnetophoresis can magnify the difference between normal and abnormal cells, such that a sorted sample has over twice the proportion of desirable cells. In addition, we demonstrated that the predictions from machine learning gave comparable results with significantly lower computational costs.
URI: https://hdl.handle.net/10356/90079
http://hdl.handle.net/10220/49426
DOI: http://dx.doi.org/10.3390/magnetochemistry4030031
Rights: © 2018 The Author(s). Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Journal Articles

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