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For: Li F, Li C, Revote J, Zhang Y, Webb GI, Li J, Song J, Lithgow T. GlycoMinestruct: a new bioinformatics tool for highly accurate mapping of the human N-linked and O-linked glycoproteomes by incorporating structural features. Sci Rep 2016;6:34595. [PMID: 27708373 DOI: 10.1038/srep34595] [Cited by in Crossref: 40] [Cited by in F6Publishing: 43] [Article Influence: 6.7] [Reference Citation Analysis]
Number Citing Articles
1 Li F, Fan C, Marquez-Lago TT, Leier A, Revote J, Jia C, Zhu Y, Smith AI, Webb GI, Liu Q, Wei L, Li J, Song J. PRISMOID: a comprehensive 3D structure database for post-translational modifications and mutations with functional impact. Brief Bioinform 2020;21:1069-79. [PMID: 31161204 DOI: 10.1093/bib/bbz050] [Cited by in Crossref: 11] [Cited by in F6Publishing: 12] [Article Influence: 11.0] [Reference Citation Analysis]
2 Qiang X, Chen H, Ye X, Su R, Wei L. M6AMRFS: Robust Prediction of N6-Methyladenosine Sites With Sequence-Based Features in Multiple Species. Front Genet 2018;9:495. [PMID: 30410501 DOI: 10.3389/fgene.2018.00495] [Cited by in Crossref: 42] [Cited by in F6Publishing: 33] [Article Influence: 10.5] [Reference Citation Analysis]
3 Xie Y, Deng C. Designed synthesis of a "One for Two" hydrophilic magnetic amino-functionalized metal-organic framework for highly efficient enrichment of glycopeptides and phosphopeptides. Sci Rep 2017;7:1162. [PMID: 28442774 DOI: 10.1038/s41598-017-01341-y] [Cited by in Crossref: 42] [Cited by in F6Publishing: 36] [Article Influence: 8.4] [Reference Citation Analysis]
4 [DOI: 10.1101/523308] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
5 Zhu Y, Jia C, Li F, Song J. Inspector: a lysine succinylation predictor based on edited nearest-neighbor undersampling and adaptive synthetic oversampling. Analytical Biochemistry 2020;593:113592. [DOI: 10.1016/j.ab.2020.113592] [Cited by in Crossref: 11] [Cited by in F6Publishing: 9] [Article Influence: 5.5] [Reference Citation Analysis]
6 Pauthner MG, Hangartner L. Broadly Neutralizing Antibodies to Highly Antigenically Variable Viruses as Templates for Vaccine Design. Curr Top Microbiol Immunol 2020;428:31-87. [PMID: 32648034 DOI: 10.1007/82_2020_221] [Reference Citation Analysis]
7 Yu WH, Su D, Torabi J, Fennessey CM, Shiakolas A, Lynch R, Chun TW, Doria-Rose N, Alter G, Seaman MS, Keele BF, Lauffenburger DA, Julg B. Predicting the broadly neutralizing antibody susceptibility of the HIV reservoir. JCI Insight 2019;4:130153. [PMID: 31484826 DOI: 10.1172/jci.insight.130153] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 1.7] [Reference Citation Analysis]
8 Wei L, Luan S, Nagai LAE, Su R, Zou Q. Exploring sequence-based features for the improved prediction of DNA N4-methylcytosine sites in multiple species. Bioinformatics 2019;35:1326-33. [PMID: 30239627 DOI: 10.1093/bioinformatics/bty824] [Cited by in Crossref: 71] [Cited by in F6Publishing: 62] [Article Influence: 35.5] [Reference Citation Analysis]
9 Li F, Wang Y, Li C, Marquez-Lago TT, Leier A, Rawlings ND, Haffari G, Revote J, Akutsu T, Chou KC, Purcell AW, Pike RN, Webb GI, Ian Smith A, Lithgow T, Daly RJ, Whisstock JC, Song J. Twenty years of bioinformatics research for protease-specific substrate and cleavage site prediction: a comprehensive revisit and benchmarking of existing methods. Brief Bioinform 2019;20:2150-66. [PMID: 30184176 DOI: 10.1093/bib/bby077] [Cited by in Crossref: 36] [Cited by in F6Publishing: 36] [Article Influence: 18.0] [Reference Citation Analysis]
10 Jia C, Zuo Y, Zou Q, Hancock J. O-GlcNAcPRED-II: an integrated classification algorithm for identifying O-GlcNAcylation sites based on fuzzy undersampling and a K-means PCA oversampling technique. Bioinformatics 2018;34:2029-36. [DOI: 10.1093/bioinformatics/bty039] [Cited by in Crossref: 73] [Cited by in F6Publishing: 59] [Article Influence: 18.3] [Reference Citation Analysis]
11 Pakhrin SC, Aoki-Kinoshita KF, Caragea D, Kc DB. DeepNGlyPred: A Deep Neural Network-Based Approach for Human N-Linked Glycosylation Site Prediction. Molecules 2021;26:7314. [PMID: 34885895 DOI: 10.3390/molecules26237314] [Reference Citation Analysis]
12 Mutalik SP, Gupton SL. Glycosylation in Axonal Guidance. Int J Mol Sci 2021;22:5143. [PMID: 34068002 DOI: 10.3390/ijms22105143] [Reference Citation Analysis]
13 Zardadi S, Razmara E, Asgaritarghi G, Jafarinia E, Bitarafan F, Rayat S, Almadani N, Morovvati S, Garshasbi M. Novel homozygous variants in the TMC1 and CDH23 genes cause autosomal recessive nonsyndromic hearing loss. Mol Genet Genomic Med 2020;8:e1550. [PMID: 33205915 DOI: 10.1002/mgg3.1550] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
14 Jia C, Bi Y, Chen J, Leier A, Li F, Song J, Mathelier A. PASSION: an ensemble neural network approach for identifying the binding sites of RBPs on circRNAs. Bioinformatics 2020;36:4276-82. [DOI: 10.1093/bioinformatics/btaa522] [Cited by in Crossref: 20] [Cited by in F6Publishing: 17] [Article Influence: 10.0] [Reference Citation Analysis]
15 Gilani N, Razmara E, Ozaslan M, Abdulzahra IK, Arzhang S, Tavasoli AR, Garshasbi M. A novel deletion variant in CLN3 with highly variable expressivity is responsible for juvenile neuronal ceroid lipofuscinoses. Acta Neurol Belg 2021;121:737-48. [PMID: 33783722 DOI: 10.1007/s13760-021-01655-9] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
16 Li P, Zhang H, Zhao X, Jia C, Li F, Song J. Pippin: A random forest-based method for identifying presynaptic and postsynaptic neurotoxins. J Bioinform Comput Biol 2020;18:2050008. [PMID: 32372714 DOI: 10.1142/S0219720020500080] [Reference Citation Analysis]
17 Song J, Wang Y, Li F, Akutsu T, Rawlings ND, Webb GI, Chou KC. iProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites. Brief Bioinform 2019;20:638-58. [PMID: 29897410 DOI: 10.1093/bib/bby028] [Cited by in Crossref: 96] [Cited by in F6Publishing: 82] [Article Influence: 48.0] [Reference Citation Analysis]
18 Pitti T, Chen CT, Lin HN, Choong WK, Hsu WL, Sung TY. N-GlyDE: a two-stage N-linked glycosylation site prediction incorporating gapped dipeptides and pattern-based encoding. Sci Rep 2019;9:15975. [PMID: 31685900 DOI: 10.1038/s41598-019-52341-z] [Cited by in Crossref: 20] [Cited by in F6Publishing: 17] [Article Influence: 6.7] [Reference Citation Analysis]
19 Meng C, Guo F, Zou Q. CWLy-SVM: A support vector machine-based tool for identifying cell wall lytic enzymes. Computational Biology and Chemistry 2020;87:107304. [DOI: 10.1016/j.compbiolchem.2020.107304] [Cited by in Crossref: 10] [Cited by in F6Publishing: 9] [Article Influence: 5.0] [Reference Citation Analysis]
20 Li F, Chen J, Leier A, Marquez-Lago T, Liu Q, Wang Y, Revote J, Smith AI, Akutsu T, Webb GI, Kurgan L, Song J. DeepCleave: a deep learning predictor for caspase and matrix metalloprotease substrates and cleavage sites. Bioinformatics 2020;36:1057-65. [PMID: 31566664 DOI: 10.1093/bioinformatics/btz721] [Cited by in Crossref: 42] [Cited by in F6Publishing: 38] [Article Influence: 21.0] [Reference Citation Analysis]
21 Pauwels J, Fijałkowska D, Eyckerman S, Gevaert K. Mass spectrometry and the cellular surfaceome. Mass Spec Rev. [DOI: 10.1002/mas.21690] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
22 Li F, Li C, Marquez-Lago TT, Leier A, Akutsu T, Purcell AW, Ian Smith A, Lithgow T, Daly RJ, Song J, Chou KC. Quokka: a comprehensive tool for rapid and accurate prediction of kinase family-specific phosphorylation sites in the human proteome. Bioinformatics 2018;34:4223-31. [PMID: 29947803 DOI: 10.1093/bioinformatics/bty522] [Cited by in Crossref: 86] [Cited by in F6Publishing: 81] [Article Influence: 28.7] [Reference Citation Analysis]
23 Li F, Guo X, Jin P, Chen J, Xiang D, Song J, Coin LJM. Porpoise: a new approach for accurate prediction of RNA pseudouridine sites. Brief Bioinform 2021:bbab245. [PMID: 34226915 DOI: 10.1093/bib/bbab245] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
24 Song J, Wang Y, Li F, Akutsu T, Rawlings ND, Webb GI, Chou K. iProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites. Briefings in Bioinformatics 2019;20:638-58. [DOI: 10.1093/bib/bby028] [Reference Citation Analysis]
25 Zhang M, Li F, Marquez-Lago TT, Leier A, Fan C, Kwoh CK, Chou KC, Song J, Jia C. MULTiPly: a novel multi-layer predictor for discovering general and specific types of promoters. Bioinformatics 2019;35:2957-65. [PMID: 30649179 DOI: 10.1093/bioinformatics/btz016] [Cited by in Crossref: 46] [Cited by in F6Publishing: 33] [Article Influence: 23.0] [Reference Citation Analysis]
26 He W, Wei L, Zou Q. Research progress in protein posttranslational modification site prediction. Briefings in Functional Genomics 2019;18:220-9. [DOI: 10.1093/bfgp/ely039] [Cited by in Crossref: 19] [Cited by in F6Publishing: 16] [Article Influence: 4.8] [Reference Citation Analysis]
27 Li F, Chen J, Ge Z, Wen Y, Yue Y, Hayashida M, Baggag A, Bensmail H, Song J. Computational prediction and interpretation of both general and specific types of promoters in Escherichia coli by exploiting a stacked ensemble-learning framework. Brief Bioinform 2021;22:2126-40. [PMID: 32363397 DOI: 10.1093/bib/bbaa049] [Cited by in Crossref: 8] [Cited by in F6Publishing: 9] [Article Influence: 4.0] [Reference Citation Analysis]
28 Xu ZC, Feng PM, Yang H, Qiu WR, Chen W, Lin H. iRNAD: a computational tool for identifying D modification sites in RNA sequence. Bioinformatics 2019;35:4922-9. [PMID: 31077296 DOI: 10.1093/bioinformatics/btz358] [Cited by in Crossref: 41] [Cited by in F6Publishing: 40] [Article Influence: 20.5] [Reference Citation Analysis]
29 Li F, Dong S, Leier A, Han M, Guo X, Xu J, Wang X, Pan S, Jia C, Zhang Y, Webb GI, Coin LJM, Li C, Song J. Positive-unlabeled learning in bioinformatics and computational biology: a brief review. Brief Bioinform 2021:bbab461. [PMID: 34729589 DOI: 10.1093/bib/bbab461] [Reference Citation Analysis]
30 Pan X, Zeng T, Zhang YH, Chen L, Feng K, Huang T, Cai YD. Investigation and Prediction of Human Interactome Based on Quantitative Features. Front Bioeng Biotechnol 2020;8:730. [PMID: 32766217 DOI: 10.3389/fbioe.2020.00730] [Cited by in Crossref: 9] [Cited by in F6Publishing: 8] [Article Influence: 4.5] [Reference Citation Analysis]
31 Zhu X, Feng C, Lai H, Chen W, Hao L. Predicting protein structural classes for low-similarity sequences by evaluating different features. Knowledge-Based Systems 2019;163:787-93. [DOI: 10.1016/j.knosys.2018.10.007] [Cited by in Crossref: 135] [Cited by in F6Publishing: 51] [Article Influence: 45.0] [Reference Citation Analysis]
32 Zhao Q, Ma J, Xie F, Wang Y, Zhang Y, Li H, Sun Y, Wang L, Guo M, Han K. Recent Advances in Predicting Protein S-Nitrosylation Sites. Biomed Res Int 2021;2021:5542224. [PMID: 33628788 DOI: 10.1155/2021/5542224] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
33 Li F, Guo X, Xiang D, Pitt ME, Bainomugisa A, Coin LJ. Computational analysis and prediction of PE_PGRS proteins using machine learning. Computational and Structural Biotechnology Journal 2022. [DOI: 10.1016/j.csbj.2022.01.019] [Reference Citation Analysis]
34 Muthu M, Chun S, Gopal J, Anthonydhason V, Haga SW, Jacintha Prameela Devadoss A, Oh JW. Insights into Bioinformatic Applications for Glycosylation: Instigating an Awakening towards Applying Glycoinformatic Resources for Cancer Diagnosis and Therapy. Int J Mol Sci 2020;21:E9336. [PMID: 33302373 DOI: 10.3390/ijms21249336] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
35 Li F, Leier A, Liu Q, Wang Y, Xiang D, Akutsu T, Webb GI, Smith AI, Marquez-Lago T, Li J, Song J. Procleave: Predicting Protease-specific Substrate Cleavage Sites by Combining Sequence and Structural Information. Genomics Proteomics Bioinformatics 2020;18:52-64. [PMID: 32413515 DOI: 10.1016/j.gpb.2019.08.002] [Cited by in Crossref: 11] [Cited by in F6Publishing: 13] [Article Influence: 5.5] [Reference Citation Analysis]
36 Liang X, Li F, Chen J, Li J, Wu H, Li S, Song J, Liu Q. Large-scale comparative review and assessment of computational methods for anti-cancer peptide identification. Brief Bioinform 2021;22:bbaa312. [PMID: 33316035 DOI: 10.1093/bib/bbaa312] [Cited by in Crossref: 4] [Cited by in F6Publishing: 6] [Article Influence: 2.0] [Reference Citation Analysis]
37 Delmar JA, Wang J, Choi SW, Martins JA, Mikhail JP. Machine Learning Enables Accurate Prediction of Asparagine Deamidation Probability and Rate. Mol Ther Methods Clin Dev 2019;15:264-74. [PMID: 31890727 DOI: 10.1016/j.omtm.2019.09.008] [Cited by in Crossref: 14] [Cited by in F6Publishing: 7] [Article Influence: 4.7] [Reference Citation Analysis]
38 Li F, Zhang Y, Purcell AW, Webb GI, Chou KC, Lithgow T, Li C, Song J. Positive-unlabelled learning of glycosylation sites in the human proteome. BMC Bioinformatics 2019;20:112. [PMID: 30841845 DOI: 10.1186/s12859-019-2700-1] [Cited by in Crossref: 36] [Cited by in F6Publishing: 32] [Article Influence: 12.0] [Reference Citation Analysis]
39 Mei S, Ayala R, Ramarathinam SH, Illing PT, Faridi P, Song J, Purcell AW, Croft NP. Immunopeptidomic Analysis Reveals That Deamidated HLA-bound Peptides Arise Predominantly from Deglycosylated Precursors. Mol Cell Proteomics 2020;19:1236-47. [PMID: 32357974 DOI: 10.1074/mcp.RA119.001846] [Cited by in Crossref: 13] [Cited by in F6Publishing: 5] [Article Influence: 6.5] [Reference Citation Analysis]
40 Dao F, Lv H, Wang F, Feng C, Ding H, Chen W, Lin H, Hancock J. Identify origin of replication in Saccharomyces cerevisiae using two-step feature selection technique. Bioinformatics 2019;35:2075-83. [DOI: 10.1093/bioinformatics/bty943] [Cited by in Crossref: 112] [Cited by in F6Publishing: 93] [Article Influence: 28.0] [Reference Citation Analysis]
41 Subedi GP, Falconer DJ, Barb AW. Carbohydrate-Polypeptide Contacts in the Antibody Receptor CD16A Identified through Solution NMR Spectroscopy. Biochemistry 2017;56:3174-7. [PMID: 28613884 DOI: 10.1021/acs.biochem.7b00392] [Cited by in Crossref: 24] [Cited by in F6Publishing: 23] [Article Influence: 4.8] [Reference Citation Analysis]
42 Wang X, Li C, Li F, Sharma VS, Song J, Webb GI. SIMLIN: a bioinformatics tool for prediction of S-sulphenylation in the human proteome based on multi-stage ensemble-learning models. BMC Bioinformatics 2019;20:602. [PMID: 31752668 DOI: 10.1186/s12859-019-3178-6] [Cited by in Crossref: 4] [Cited by in F6Publishing: 6] [Article Influence: 1.3] [Reference Citation Analysis]
43 Feng CQ, Zhang ZY, Zhu XJ, Lin Y, Chen W, Tang H, Lin H. iTerm-PseKNC: a sequence-based tool for predicting bacterial transcriptional terminators. Bioinformatics 2019;35:1469-77. [PMID: 30247625 DOI: 10.1093/bioinformatics/bty827] [Cited by in Crossref: 120] [Cited by in F6Publishing: 101] [Article Influence: 60.0] [Reference Citation Analysis]
44 Zhu YH, Hu J, Song XN, Yu DJ. DNAPred: Accurate Identification of DNA-Binding Sites from Protein Sequence by Ensembled Hyperplane-Distance-Based Support Vector Machines. J Chem Inf Model 2019;59:3057-71. [PMID: 30943723 DOI: 10.1021/acs.jcim.8b00749] [Cited by in Crossref: 15] [Cited by in F6Publishing: 10] [Article Influence: 5.0] [Reference Citation Analysis]
45 Liu Y, Wang X, Liu B. IDP⁻CRF: Intrinsically Disordered Protein/Region Identification Based on Conditional Random Fields. Int J Mol Sci 2018;19:E2483. [PMID: 30135358 DOI: 10.3390/ijms19092483] [Cited by in Crossref: 12] [Cited by in F6Publishing: 10] [Article Influence: 3.0] [Reference Citation Analysis]
46 Pujić I, Perreault H. Recent advancements in glycoproteomic studies: Glycopeptide enrichment and derivatization, characterization of glycosylation in SARS CoV2, and interacting glycoproteins. Mass Spectrom Rev 2021. [PMID: 33393161 DOI: 10.1002/mas.21679] [Cited by in Crossref: 3] [Article Influence: 3.0] [Reference Citation Analysis]