scholarly journals Neural network fast‐classifies biological images through features selecting to power automated microscopy

2021 ◽  
Author(s):  
Maël Balluet ◽  
Florian Sizaire ◽  
Youssef El Habouz ◽  
Thomas Walter ◽  
Jérémy Pont ◽  
...  
Keyword(s):  
Neural Network ◽  
Biological Images ◽  
Automated Microscopy

A Similarity Searching System for Biological Phenotype Images Using Deep Convolutional Encoder-decoder Architecture

2019 ◽  
Vol 14 (7) ◽  
pp. 628-639 ◽  
Author(s):  
Bizhi Wu ◽  
Hangxiao Zhang ◽  
Limei Lin ◽  
Huiyuan Wang ◽  
Yubang Gao ◽  
...  
Keyword(s):  
Neural Network ◽  
Retrieval System ◽  
Sequence Similarity ◽  
Local Alignment ◽  
Similarity Searching ◽  
Loss Of Function ◽  
Biological Images ◽  
The Neural Network ◽  
Convolutional Autoencoder ◽  
Biological Phenotype

Background: The BLAST (Basic Local Alignment Search Tool) algorithm has been widely used for sequence similarity searching. Analogously, the public phenotype images must be efficiently retrieved using biological images as queries and identify the phenotype with high similarity. Due to the accumulation of genotype-phenotype-mapping data, a system of searching for similar phenotypes is not available due to the bottleneck of image processing. Objective: In this study, we focus on the identification of similar query phenotypic images by searching the biological phenotype database, including information about loss-of-function and gain-of-function. Methods: We propose a deep convolutional autoencoder architecture to segment the biological phenotypic images and develop a phenotype retrieval system to enable a better understanding of genotype–phenotype correlation. Results: This study shows how deep convolutional autoencoder architecture can be trained on images from biological phenotypes to achieve state-of-the-art performance in a phenotypic images retrieval system. Conclusion: Taken together, the phenotype analysis system can provide further information on the correlation between genotype and phenotype. Additionally, it is obvious that the neural network model of image segmentation and the phenotype retrieval system is equally suitable for any species, which has enough phenotype images to train the neural network.

scholarly journals Machine learning and computer vision approaches for phenotypic profiling

2016 ◽  
Vol 216 (1) ◽  
pp. 65-71 ◽  
Author(s):  
Ben T. Grys ◽  
Dara S. Lo ◽  
Nil Sahin ◽  
Oren Z. Kraus ◽  
Quaid Morris ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Feature Extraction ◽  
High Throughput ◽  
Large Scale ◽  
Cell Segmentation ◽  
Learning Approaches ◽  
Biological Images ◽  
Machine Learning Methods ◽  
Automated Microscopy

With recent advances in high-throughput, automated microscopy, there has been an increased demand for effective computational strategies to analyze large-scale, image-based data. To this end, computer vision approaches have been applied to cell segmentation and feature extraction, whereas machine-learning approaches have been developed to aid in phenotypic classification and clustering of data acquired from biological images. Here, we provide an overview of the commonly used computer vision and machine-learning methods for generating and categorizing phenotypic profiles, highlighting the general biological utility of each approach.

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural
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