2020
DOI: 10.1093/bib/bbaa301
|View full text |Cite
|
Sign up to set email alerts
|

DeepBL: a deep learning-based approach for in silico discovery of beta-lactamases

Abstract: Beta-lactamases (BLs) are enzymes localized in the periplasmic space of bacterial pathogens, where they confer resistance to beta-lactam antibiotics. Experimental identification of BLs is costly yet crucial to understand beta-lactam resistance mechanisms. To address this issue, we present DeepBL, a deep learning-based approach by incorporating sequence-derived features to enable high-throughput prediction of BLs. Specifically, DeepBL is implemented based on the Small VGGNet architecture and the TensorFlow deep… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
12
0

Year Published

2021
2021
2023
2023

Publication Types

Select...
6
1

Relationship

1
6

Authors

Journals

citations
Cited by 11 publications
(12 citation statements)
references
References 42 publications
0
12
0
Order By: Relevance
“…Recently, deep learning has been used to solve various problems in bioinformatics ( Li et al, 2019 ; Tang et al, 2019 ; Chaudhari et al, 2020 ; Thapa et al, 2020 ; Wang D. et al, 2020 ; Wang Y. et al, 2020 ). One of the most serious problems associated with deep learning stems from data dependence.…”
Section: Methodsmentioning
confidence: 99%
“…Recently, deep learning has been used to solve various problems in bioinformatics ( Li et al, 2019 ; Tang et al, 2019 ; Chaudhari et al, 2020 ; Thapa et al, 2020 ; Wang D. et al, 2020 ; Wang Y. et al, 2020 ). One of the most serious problems associated with deep learning stems from data dependence.…”
Section: Methodsmentioning
confidence: 99%
“…Lee et al (2019) proposed the use of three automatic encoders and a deep feed-forward network to predict DDI. Structural similarity profiles (SSP), target gene similarity profiles (TSP) and Gene Ontology (GO) term similarity profiles (GSP) were measured by autoencoder for dimension reduction (Wang et al, 2020b;Zeng et al, 2020b;Wang Y et al, 2020). The three autoencoders are all isomorphic, the size of input layer and output layer are 3194 and 600 respectively, and the size of hidden layer are 1000, 200 and 1000 respectively.…”
Section: Graph-embedding Approachmentioning
confidence: 99%
“…It is based on well-interpreted massive RefSeq datasets covering > 39 K BLs extracted from the NCBI database. Comparing this model with the other conventional machine learning-based algorithms, including SVM, RF, NB and LR, DeepBL outperformed them after evaluation on an independent test set comprising more than 10 K sequences [ 98 ]. Until very recently, deep learning applications in pharmacogenomics remained under consideration.…”
Section: Deep Learning Tools/software/pipelines In Genomicsmentioning
confidence: 99%