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.
Query-seeded iterative sequence similarity searching improves selectivity 5–20-fold
