DeepBL: a deep learning-based approach for <i>in silico</i> discovery of beta-lactamases

Wang, Yanan; Li, Fuyi; Bharathwaj, Manasa; Rosas, Natalia C.; Leier, André; Akutsu, Tatsuya; Webb, Geoffrey I.; Marquez‐Lago, Tatiana T.; Li, Jian; Lithgow, Trevor; Song, Jiangning

doi:10.1093/bib/bbaa301

Cited by 11 publications

(12 citation statements)

References 42 publications

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“…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%

DTL-DephosSite: Deep Transfer Learning Based Approach to Predict Dephosphorylation Sites

Chaudhari

Thapa

Ismail

et al. 2021

Front. Cell Dev. Biol.

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Phosphorylation, which is mediated by protein kinases and opposed by protein phosphatases, is an important post-translational modification that regulates many cellular processes, including cellular metabolism, cell migration, and cell division. Due to its essential role in cellular physiology, a great deal of attention has been devoted to identifying sites of phosphorylation on cellular proteins and understanding how modification of these sites affects their cellular functions. This has led to the development of several computational methods designed to predict sites of phosphorylation based on a protein’s primary amino acid sequence. In contrast, much less attention has been paid to dephosphorylation and its role in regulating the phosphorylation status of proteins inside cells. Indeed, to date, dephosphorylation site prediction tools have been restricted to a few tyrosine phosphatases. To fill this knowledge gap, we have employed a transfer learning strategy to develop a deep learning-based model to predict sites that are likely to be dephosphorylated. Based on independent test results, our model, which we termed DTL-DephosSite, achieved efficiency scores for phosphoserine/phosphothreonine residues of 84%, 84% and 0.68 with respect to sensitivity (SN), specificity (SP) and Matthew’s correlation coefficient (MCC). Similarly, DTL-DephosSite exhibited efficiency scores of 75%, 88% and 0.64 for phosphotyrosine residues with respect to SN, SP, and MCC.

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Section: Methodsmentioning

confidence: 99%

DTL-DephosSite: Deep Transfer Learning Based Approach to Predict Dephosphorylation Sites

Chaudhari

Thapa

Ismail

et al. 2021

Front. Cell Dev. Biol.

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“…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%

A Review of Approaches for Predicting Drug–Drug Interactions Based on Machine Learning

Han

Cao²,

Wang

et al. 2022

Front. Pharmacol.

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Drug–drug interactions play a vital role in drug research. However, they may also cause adverse reactions in patients, with serious consequences. Manual detection of drug–drug interactions is time-consuming and expensive, so it is urgent to use computer methods to solve the problem. There are two ways for computers to identify drug interactions: one is to identify known drug interactions, and the other is to predict unknown drug interactions. In this paper, we review the research progress of machine learning in predicting unknown drug interactions. Among these methods, the literature-based method is special because it combines the extraction method of DDI and the prediction method of DDI. We first introduce the common databases, then briefly describe each method, and summarize the advantages and disadvantages of some prediction models. Finally, we discuss the challenges and prospects of machine learning methods in predicting drug interactions. This review aims to provide useful guidance for interested researchers to further promote bioinformatics algorithms to predict DDI.

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“…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%

A review of deep learning applications in human genomics using next-generation sequencing data

Alharbi

Rashid

2022

Hum Genomics

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Genomics is advancing towards data-driven science. Through the advent of high-throughput data generating technologies in human genomics, we are overwhelmed with the heap of genomic data. To extract knowledge and pattern out of this genomic data, artificial intelligence especially deep learning methods has been instrumental. In the current review, we address development and application of deep learning methods/models in different subarea of human genomics. We assessed over- and under-charted area of genomics by deep learning techniques. Deep learning algorithms underlying the genomic tools have been discussed briefly in later part of this review. Finally, we discussed briefly about the late application of deep learning tools in genomic. Conclusively, this review is timely for biotechnology or genomic scientists in order to guide them why, when and how to use deep learning methods to analyse human genomic data.

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

Cited by 11 publications

References 42 publications

DTL-DephosSite: Deep Transfer Learning Based Approach to Predict Dephosphorylation Sites

DTL-DephosSite: Deep Transfer Learning Based Approach to Predict Dephosphorylation Sites

A Review of Approaches for Predicting Drug–Drug Interactions Based on Machine Learning

A review of deep learning applications in human genomics using next-generation sequencing data

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