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Title: Modeling Cell Migration with Convolutional Neural Network and Deep Reinforcement Learning

Abstract

Cell migration modeling is a longstanding biological challenge, which is regulated by a highly complex set of regulatory mechanisms at multiple scales in a developmental system. This study presents a generic framework for regulatory mechanisms discovery during cell migration. This framework uses convolutional neural networks and reinforcement learning to better study navigation rules and mechanisms during cell migration. This framework adopts a flexible model-free approach that directly takes raw images as the sensory input. It can better handle simulation scenarios that involve cell division during embryogenesis. Computational experiments also prove that this model achieves better performance than a previous model with a fully connected neural network.

Authors:
 [1]; ORCiD logo [1];  [2];  [3];  [2];  [3]
  1. ORNL
  2. The University of Tennessee, Knoxville
  3. Sloan-Kettering Institute, NY
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
OSTI Identifier:
1543193
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: ICML 2019 Workshop on Computational Biology - Long Island, California, United States of America - 6/10/2019 8:00:00 AM-6/14/2019 8:00:00 AM
Country of Publication:
United States
Language:
English

Citation Formats

Wang, Zi, Wang, Dali, Li, Chengcheng, Xu, Yichi, Li, Husheng, and Bao, Zhirong. Modeling Cell Migration with Convolutional Neural Network and Deep Reinforcement Learning. United States: N. p., 2019. Web.
Wang, Zi, Wang, Dali, Li, Chengcheng, Xu, Yichi, Li, Husheng, & Bao, Zhirong. Modeling Cell Migration with Convolutional Neural Network and Deep Reinforcement Learning. United States.
Wang, Zi, Wang, Dali, Li, Chengcheng, Xu, Yichi, Li, Husheng, and Bao, Zhirong. Sat . "Modeling Cell Migration with Convolutional Neural Network and Deep Reinforcement Learning". United States. https://www.osti.gov/servlets/purl/1543193.
@article{osti_1543193,
title = {Modeling Cell Migration with Convolutional Neural Network and Deep Reinforcement Learning},
author = {Wang, Zi and Wang, Dali and Li, Chengcheng and Xu, Yichi and Li, Husheng and Bao, Zhirong},
abstractNote = {Cell migration modeling is a longstanding biological challenge, which is regulated by a highly complex set of regulatory mechanisms at multiple scales in a developmental system. This study presents a generic framework for regulatory mechanisms discovery during cell migration. This framework uses convolutional neural networks and reinforcement learning to better study navigation rules and mechanisms during cell migration. This framework adopts a flexible model-free approach that directly takes raw images as the sensory input. It can better handle simulation scenarios that involve cell division during embryogenesis. Computational experiments also prove that this model achieves better performance than a previous model with a fully connected neural network.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {6}
}

Conference:
Other availability
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