BPG is committed to discovery and dissemination of knowledge
Cited by in F6Publishing
For: Tao Z, Li Y, Teng Z, Zhao Y. A Method for Identifying Vesicle Transport Proteins Based on LibSVM and MRMD. Comput Math Methods Med 2020;2020:8926750. [PMID: 33133228 DOI: 10.1155/2020/8926750] [Cited by in Crossref: 30] [Cited by in F6Publishing: 36] [Article Influence: 15.0] [Reference Citation Analysis]
Number Citing Articles
1 Wang Y, Zhang Y, Zhang Y, Gu Z, Zhang Z, Lin H, Deng K. Identification of adaptor proteins using the ANOVA feature selection technique. Methods 2022;208:42-47. [DOI: 10.1016/j.ymeth.2022.10.008] [Reference Citation Analysis]
2 Xiao Z, Wang L, Ding Y, Yu L. iEnhancer-MRBF: Identifying enhancers and their strength with a multiple Laplacian-regularized radial basis function network. Methods 2022;208:1-8. [DOI: 10.1016/j.ymeth.2022.10.001] [Reference Citation Analysis]
3 Gao W, Xu D, Li H, Du J, Wang G, Li D. Identification of adaptor proteins by incorporating deep learning and PSSM profiles. Methods 2022. [DOI: 10.1016/j.ymeth.2022.11.001] [Reference Citation Analysis]
4 Xiao J, Liu M, Huang Q, Sun Z, Ning L, Duan J, Zhu S, Huang J, Lin H, Yang H. Analysis and modeling of myopia-related factors based on questionnaire survey. Computers in Biology and Medicine 2022;150:106162. [DOI: 10.1016/j.compbiomed.2022.106162] [Reference Citation Analysis]
5 Zhao S, Zhang Y, Ding Y, Zou Q, Tang L, Liu Q, Zhang Y. Identification of DNA-binding proteins via Multi-view LSSVM with independence criterion. Methods 2022;207:29-37. [PMID: 36087888 DOI: 10.1016/j.ymeth.2022.08.015] [Reference Citation Analysis]
6 Fan R, Suo B, Ding Y. Identification of Vesicle Transport Proteins via Hypergraph Regularized K-Local Hyperplane Distance Nearest Neighbour Model. Front Genet 2022;13:960388. [DOI: 10.3389/fgene.2022.960388] [Reference Citation Analysis]
7 Zhao S, Pan Q, Zou Q, Ju Y, Shi L, Su X, Liao C. Identifying and Classifying Enhancers by Dinucleotide-Based Auto-Cross Covariance and Attention-Based Bi-LSTM. Computational and Mathematical Methods in Medicine 2022;2022:1-11. [DOI: 10.1155/2022/7518779] [Reference Citation Analysis]
8 Li H, Pang Y, Liu B, Yu L. MoRF-FUNCpred: Molecular Recognition Feature Function Prediction Based on Multi-Label Learning and Ensemble Learning. Front Pharmacol 2022;13:856417. [PMID: 35350759 DOI: 10.3389/fphar.2022.856417] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
9 Meng C, Ju Y, Shi H. TMPpred: A support vector machine-based thermophilic protein identifier. Anal Biochem 2022;:114625. [PMID: 35218736 DOI: 10.1016/j.ab.2022.114625] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
10 Zhang S, Jiang H, Gao B, Yang W, Wang G. Identification of Diagnostic Markers for Breast Cancer Based on Differential Gene Expression and Pathway Network. Front Cell Dev Biol 2021;9:811585. [PMID: 35096840 DOI: 10.3389/fcell.2021.811585] [Reference Citation Analysis]
11 Ma D, Chen Z, He Z, Huang X. A SNARE Protein Identification Method Based on iLearnPlus to Efficiently Solve the Data Imbalance Problem. Front Genet 2022;12:818841. [DOI: 10.3389/fgene.2021.818841] [Reference Citation Analysis]
12 Zhao Z, Yang W, Zhai Y, Liang Y, Zhao Y. Identify DNA-Binding Proteins Through the Extreme Gradient Boosting Algorithm. Front Genet 2022;12:821996. [DOI: 10.3389/fgene.2021.821996] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 6.0] [Reference Citation Analysis]
13 Wan H, Zhang J, Ding Y, Wang H, Tian G. Immunoglobulin Classification Based on FC* and GC* Features. Front Genet 2022;12:827161. [DOI: 10.3389/fgene.2021.827161] [Reference Citation Analysis]
14 Gong Y, Dong B, Zhang Z, Zhai Y, Gao B, Zhang T, Zhang J. VTP-Identifier: Vesicular Transport Proteins Identification Based on PSSM Profiles and XGBoost. Front Genet 2021;12:808856. [PMID: 35047020 DOI: 10.3389/fgene.2021.808856] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
15 Lin C, Wang L, Shi L. AAPred-CNN: accurate predictor based on deep convolution neural network for identification of anti-angiogenic peptides. Methods 2022:S1046-2023(22)00010-X. [PMID: 35031486 DOI: 10.1016/j.ymeth.2022.01.004] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
16 Zhang Z, Gong Y, Gao B, Li H, Gao W, Zhao Y, Dong B. SNAREs-SAP: SNARE Proteins Identification With PSSM Profiles. Front Genet 2021;12:809001. [PMID: 34987554 DOI: 10.3389/fgene.2021.809001] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
17 Chen Y, Juan L, Lv X, Shi L. Bioinformatics Research on Drug Sensitivity Prediction. Front Pharmacol 2021;12:799712. [PMID: 34955863 DOI: 10.3389/fphar.2021.799712] [Reference Citation Analysis]
18 Guo Y, Ju Y, Chen D, Wang L. Research on the Computational Prediction of Essential Genes. Front Cell Dev Biol 2021;9:803608. [PMID: 34938741 DOI: 10.3389/fcell.2021.803608] [Reference Citation Analysis]
19 Gu X, Guo L, Liao B, Jiang Q. Pseudo-188D: Phage Protein Prediction Based on a Model of Pseudo-188D. Front Genet 2021;12:796327. [PMID: 34925468 DOI: 10.3389/fgene.2021.796327] [Reference Citation Analysis]
20 Jia Y, Huang S, Zhang T. KK-DBP: A Multi-Feature Fusion Method for DNA-Binding Protein Identification Based on Random Forest. Front Genet 2021;12:811158. [PMID: 34912382 DOI: 10.3389/fgene.2021.811158] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
21 Teng Z, Zhang Z, Tian Z, Li Y, Wang G. ReRF-Pred: predicting amyloidogenic regions of proteins based on their pseudo amino acid composition and tripeptide composition. BMC Bioinformatics 2021;22:545. [PMID: 34753427 DOI: 10.1186/s12859-021-04446-4] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
22 Fu T, Li F, Zhang Y, Yin J, Qiu W, Li X, Liu X, Xin W, Wang C, Yu L, Gao J, Zheng Q, Zeng S, Zhu F. VARIDT 2.0: structural variability of drug transporter. Nucleic Acids Res 2021:gkab1013. [PMID: 34747471 DOI: 10.1093/nar/gkab1013] [Cited by in Crossref: 29] [Cited by in F6Publishing: 35] [Article Influence: 29.0] [Reference Citation Analysis]
23 Jiao S, Zou Q, Guo H, Shi L. iTTCA-RF: a random forest predictor for tumor T cell antigens. J Transl Med 2021;19:449. [PMID: 34706730 DOI: 10.1186/s12967-021-03084-x] [Cited by in Crossref: 12] [Cited by in F6Publishing: 12] [Article Influence: 12.0] [Reference Citation Analysis]
24 Zulfiqar H, Yuan SS, Huang QL, Sun ZJ, Dao FY, Yu XL, Lin H. Identification of cyclin protein using gradient boost decision tree algorithm. Comput Struct Biotechnol J 2021;19:4123-31. [PMID: 34527186 DOI: 10.1016/j.csbj.2021.07.013] [Cited by in Crossref: 20] [Cited by in F6Publishing: 16] [Article Influence: 20.0] [Reference Citation Analysis]
25 Jiang P, Ning W, Shi Y, Liu C, Mo S, Zhou H, Liu K, Guo Y. FSL-Kla: A few-shot learning-based multi-feature hybrid system for lactylation site prediction. Comput Struct Biotechnol J 2021;19:4497-509. [PMID: 34471495 DOI: 10.1016/j.csbj.2021.08.013] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 6.0] [Reference Citation Analysis]
26 Zeng R, Liao M. 6mAPred-MSFF: A Deep Learning Model for Predicting DNA N6-Methyladenine Sites across Species Based on a Multi-Scale Feature Fusion Mechanism. Applied Sciences 2021;11:7731. [DOI: 10.3390/app11167731] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
27 Gong Y, Liao B, Peng D, Zou Q. Accurate Prediction and Key Feature Recognition of Immunoglobulin. Applied Sciences 2021;11:6894. [DOI: 10.3390/app11156894] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
28 Zhu W, Guo Y, Zou Q. Prediction of presynaptic and postsynaptic neurotoxins based on feature extraction. Math Biosci Eng 2021;18:5943-58. [PMID: 34517517 DOI: 10.3934/mbe.2021297] [Reference Citation Analysis]
29 Xu L, Ru X, Song R. Application of Machine Learning for Drug-Target Interaction Prediction. Front Genet 2021;12:680117. [PMID: 34234813 DOI: 10.3389/fgene.2021.680117] [Cited by in Crossref: 8] [Cited by in F6Publishing: 7] [Article Influence: 8.0] [Reference Citation Analysis]
30 Ao C, Zou Q, Yu L. RFhy-m2G: Identification of RNA N2-methylguanosine modification sites based on random forest and hybrid features. Methods 2021:S1046-2023(21)00142-0. [PMID: 34033879 DOI: 10.1016/j.ymeth.2021.05.016] [Cited by in Crossref: 10] [Cited by in F6Publishing: 16] [Article Influence: 10.0] [Reference Citation Analysis]
31 Yang X, Ye X, Li X, Wei L. iDNA-MT: Identification DNA Modification Sites in Multiple Species by Using Multi-Task Learning Based a Neural Network Tool. Front Genet 2021;12:663572. [PMID: 33868390 DOI: 10.3389/fgene.2021.663572] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
32 Lv Y, Huang S, Zhang T, Gao B. Application of Multilayer Network Models in Bioinformatics. Front Genet 2021;12:664860. [PMID: 33868392 DOI: 10.3389/fgene.2021.664860] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
33 Xu L, Jiao S, Zhang D, Wu S, Zhang H, Gao B. Identification of long noncoding RNAs with machine learning methods: a review. Brief Funct Genomics 2021;20:174-80. [PMID: 33758917 DOI: 10.1093/bfgp/elab017] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
34 Jiang P, Ning W, Shi Y, Liu C, Mo S, Zhou H, Liu K, Guo Y. FSL-Kla: A few-shot learning-based multi-feature hybrid system for lactylation site prediction. Computational and Structural Biotechnology Journal 2021;19:4497-509. [DOI: 10.1016/j.csbj.2021.08.013] [Cited by in F6Publishing: 1] [Reference Citation Analysis]