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Electrophoresis. 2016 Aug;37(15-16):2208-16. doi: 10.1002/elps.201600197. Epub 2016 Jul 4.

Optimal processing for gel electrophoresis images: Applying Monte Carlo Tree Search in GelApp.

Author information

1
Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.
2
Sorbonne Universités, Paris, France.
3
UPMC Univ. Paris 06, UJF, CNRS, IMT, NUS, Image and Pervasive Access Lab (IPAL), Singapore.
4
p53 Laboratory, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.
5
Department of Computer Science, National University of Singapore (NUS), Singapore.

Abstract

In biomedical research, gel band size estimation in electrophoresis analysis is a routine process. To facilitate and automate this process, numerous software have been released, notably the GelApp mobile app. However, the band detection accuracy is limited due to a band detection algorithm that cannot adapt to the variations in input images. To address this, we used the Monte Carlo Tree Search with Upper Confidence Bound (MCTS-UCB) method to efficiently search for optimal image processing pipelines for the band detection task, thereby improving the segmentation algorithm. Incorporating this into GelApp, we report a significant enhancement of gel band detection accuracy by 55.9 ± 2.0% for protein polyacrylamide gels, and 35.9 ± 2.5% for DNA SYBR green agarose gels. This implementation is a proof-of-concept in demonstrating MCTS-UCB as a strategy to optimize general image segmentation. The improved version of GelApp-GelApp 2.0-is freely available on both Google Play Store (for Android platform), and Apple App Store (for iOS platform).

KEYWORDS:

Band size estimation; Gel electrophoresis; Image processing; Image segmentation; Monte Carlo Tree Search with Upper Confidence Bound

PMID:
27251892
DOI:
10.1002/elps.201600197
[Indexed for MEDLINE]

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