DeepIPs: comprehensive assessment and computational identification of phosphorylation sites of SARS-CoV-2 infection using a deep learning-based approach

Lv, Hao; Dao, Fanny; Zulfiqar, Hasan; Lin, Hao

doi:10.1093/bib/bbab244

Cited by 62 publications

(33 citation statements)

References 44 publications

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“…There are some common findings, but also considerable differences between studies (Kamo et al, 2017;Luedde et al, 2017;Cui et al, 2018;Kummen et al, 2018;Mayerhofer et al, 2018Mayerhofer et al, , 2020Iqubal et al, 2020;Khan et al, 2020;Qi et al, 2021). Some computational methods have been applied in the field and other biological data (Long et al, 2021;Lv et al, 2021;Yang et al, 2021). Thus, more studies are still needed to provide detailed information on variations of gut microbial composition and its impacts on CHF, especially the severe CHF.…”

Section: Introductionmentioning

confidence: 99%

Alterations of the Gut Microbiota in Patients With Severe Chronic Heart Failure

Sun

Du²,

et al. 2022

Front. Microbiol.

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Chronic heart failure (CHF) is the final outcome of almost all forms of cardiovascular diseases, remaining the main cause of mortality worldwide. Accumulating evidence is focused on the roles of gut microbial community in cardiovascular disease, but few studies have unveiled the alterations and further directions of gut microbiota in severe CHF patients. Aimed to investigate this deficiency, fecal samples from 29 CHF patients diagnosed with NYHA Class III-IV and 30 healthy controls were collected and then analyzed using bacterial 16S rRNA gene sequencing. As a result, there were many significant differences between the two groups. Firstly, the phylum Firmicutes was found to be remarkably decreased in severe CHF patients, and the phylum Proteobacteria was the second most abundant phyla in severe CHF patients instead of phylum Bacteroides strangely. Secondly, the α diversity indices such as chao1, PD-whole-tree and Shannon indices were significantly decreased in the severe CHF versus the control group, as well as the notable difference in β-diversity between the two groups. Thirdly, our result revealed a remarkable decrease in the abundance of the short-chain fatty acids (SCFA)-producing bacteria including genera Ruminococcaceae UCG-004, Ruminococcaceae UCG-002, Lachnospiraceae FCS020 group, Dialister and the increased abundance of the genera in Enterococcus and Enterococcaceae with an increased production of lactic acid. Finally, the alternation of the gut microbiota was presumably associated with the function including Cell cycle control, cell division, chromosome partitioning, Amino acid transport and metabolism and Carbohydrate transport and metabolism through SCFA pathway. Our findings provide the direction and theoretical knowledge for the regulation of gut flora in the treatment of severe CHF.

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Section: Introductionmentioning

confidence: 99%

Alterations of the Gut Microbiota in Patients With Severe Chronic Heart Failure

Sun

Du²,

et al. 2022

Front. Microbiol.

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“…Compared with conventional clinical calculation equations ( Chiu et al, 2005 ), the ANNs obtained better results. Deep learning ( Dao et al, 2021 ; Lv et al, 2021a , b ) also made a great contribution to the clinic, including skin cancer ( Esteva et al, 2017 ), breast cancer ( Liu J. et al, 2021 ), and brain diseases ( Liu G. et al, 2018 ; Liu et al, 2019 ; Bi et al, 2020 ; Hu et al, 2020 , 2021a , b ). In biological field, machine learning has been widely used to solve biological problems, including O -GlcNAcylation site prediction ( Jia et al, 2018 ), microbiology analysis ( Qu et al, 2019 ), microRNAs and cancer association prediction ( Zeng et al, 2018 ), lncRNAs ( Cheng et al, 2016 ; Deng et al, 2021 ), CircRNAs ( Fang et al, 2019 ; Zhao et al, 2019 ), DNA methylation site ( Wei et al, 2018b ; Zou et al, 2019 ; Dai et al, 2020 ), osteoporosis diagnoses ( Su et al, 2020b ), function prediction of proteins ( Wei et al, 2018a ; Wang H. et al, 2019 ; Deng et al, 2020b ; Ding et al, 2020a ; Su et al, 2020a ), nucleotide binding sites ( Ding et al, 2021b ), drug complex network analysis ( Ding et al, 2019 , 2020b , a ; Deng et al, 2020a ; Han et al, 2021 ; Liu H. et al, 2021 ), protein remote homology ( Liu B. et al, 2018 ), electron transport proteins ( Ru et al, 2019 ), and cell-specific replication.…”

Section: Introductionmentioning

confidence: 99%

Multiple Laplacian Regularized RBF Neural Network for Assessing Dry Weight of Patients With End-Stage Renal Disease

et al. 2021

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Dry weight (DW) is an important dialysis index for patients with end-stage renal disease. It can guide clinical hemodialysis. Brain natriuretic peptide, chest computed tomography image, ultrasound, and bioelectrical impedance analysis are key indicators (multisource information) for assessing DW. By these approaches, a trial-and-error method (traditional measurement method) is employed to assess DW. The assessment of clinician is time-consuming. In this study, we developed a method based on artificial intelligence technology to estimate patient DW. Based on the conventional radial basis function neural (RBFN) network, we propose a multiple Laplacian-regularized RBFN (MLapRBFN) model to predict DW of patient. Compared with other model and body composition monitor, our method achieves the lowest value (1.3226) of root mean square error. In Bland-Altman analysis of MLapRBFN, the number of out agreement interval is least (17 samples). MLapRBFN integrates multiple Laplace regularization terms, and employs an efficient iterative algorithm to solve the model. The ratio of out agreement interval is 3.57%, which is lower than 5%. Therefore, our method can be tentatively applied for clinical evaluation of DW in hemodialysis patients.

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“…Inspired by the complementary outcome of utilizing “PseAAC” to handle the sequences of peptide or protein, the proposed strategy of “PseAAC” was continued to Pseudo K-tuple Nucleotide Composition (PseKNC) for developing and achieving different feature vectors for RNA/DNA that have confirmed very favourable as well [ 64 – 70 ]. Especially, recently, an advanced web server named “Pse-in-One” [ 71 ] and “Pse-in-One 2.0” [ 72 ], which is its advanced version and can be utilized in generating any required protein/peptide vector and sequences of DNA and RNA according to the requirement of the users. Here are some methodologies used for extracting the features, to identify the specific arrangements associated with the primary protein structure.…”

Section: Materials and Methodologymentioning

confidence: 99%

DNAPred_Prot: Identification of DNA-Binding Proteins Using Composition- and Position-Based Features

Barukab

Khan

et al. 2022

Applied Bionics and Biomechanics

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In the domain of genome annotation, the identification of DNA-binding protein is one of the crucial challenges. DNA is considered a blueprint for the cell. It contained all necessary information for building and maintaining the trait of an organism. It is DNA, which makes a living thing, a living thing. Protein interaction with DNA performs an essential role in regulating DNA functions such as DNA repair, transcription, and regulation. Identification of these proteins is a crucial task for understanding the regulation of genes. Several methods have been developed to identify the binding sites of DNA and protein depending upon the structures and sequences, but they were costly and time-consuming. Therefore, we propose a methodology named “DNAPred_Prot”, which uses various position and frequency-dependent features from protein sequences for efficient and effective prediction of DNA-binding proteins. Using testing techniques like 10-fold cross-validation and jackknife testing an accuracy of 94.95% and 95.11% was yielded, respectively. The results of SVM and ANN were also compared with those of a random forest classifier. The robustness of the proposed model was evaluated by using the independent dataset PDB186, and an accuracy of 91.47% was achieved by it. From these results, it can be predicted that the suggested methodology performs better than other extant methods for the identification of DNA-binding proteins.

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DeepIPs: comprehensive assessment and computational identification of phosphorylation sites of SARS-CoV-2 infection using a deep learning-based approach

Cited by 62 publications

References 44 publications

Alterations of the Gut Microbiota in Patients With Severe Chronic Heart Failure

Alterations of the Gut Microbiota in Patients With Severe Chronic Heart Failure

Multiple Laplacian Regularized RBF Neural Network for Assessing Dry Weight of Patients With End-Stage Renal Disease

DNAPred_Prot: Identification of DNA-Binding Proteins Using Composition- and Position-Based Features

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