Actin Cytoskeleton Morphology Modeling Using Graph Embedding and Classification in Machine Learning

被引:3
|
作者
Liu, Yi [1 ]
Zhang, Juntao [1 ]
Bharat, Charuku [1 ]
Ren, Juan [1 ]
机构
[1] Iowa State Univ, Ames, IA 50011 USA
来源
IFAC PAPERSONLINE | 2021年 / 54卷 / 20期
基金
美国国家科学基金会;
关键词
Graph to vector embedding; Skip-gram model; Classification; Machine learning; Actin cytoskeleton; ENHANCEMENT; SCALE;
D O I
10.1016/j.ifacol.2021.11.195
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Actin cytoskeleton modeling and quantification are essential in studying the dynamics of cellular mechanotransduction. However, current approaches to actin cytoskeleton quantification are limited in terms of both efficiency and accuracy. In this paper, we propose to model the cellular actin cytoskeleton morphology using the graph to vector embedding technique together with the neural network (NN) classification in machine learning. The proposed model consists of a skip-gram model followed by a fully connected classifier. The actin cytoskeleton morphology is modeled based on both the structure and node features extracted from the cytoskeleton images. Specifically, the embedding tool outputs the embedded vectors of the cytoskeleton graphs, and then the embedded vectors are used by the fully connected layer to perform cytoskeleton classification. In this work, we demonstrate the classification accuracy of the proposed framework using actin cytoskeleton images from cells treated by Latrunculin B (an actin depolymerizer) at different concentrations. The actin cytoskeleton morphology corresponding to each treatment concentration is defined as a class (e.g., actin depolymerization level). The final classification result is showed an accuracy of 85.3%. Copyright (C) 2021 The Authors.
引用
收藏
页码:328 / 333
页数:6
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