131 related articles for article (PubMed ID: 35358650)
1. Accurately predicting nitrosylated tyrosine sites using probabilistic sequence information.
Rahman A; Ahmed S; Al Mehedi Hasan M; Ahmad S; Dehzangi I
Gene; 2022 Jun; 826():146445. PubMed ID: 35358650
[TBL] [Abstract][Full Text] [Related]
2. predCar-site: Carbonylation sites prediction in proteins using support vector machine with resolving data imbalanced issue.
Hasan MA; Li J; Ahmad S; Molla MK
Anal Biochem; 2017 May; 525():107-113. PubMed ID: 28286168
[TBL] [Abstract][Full Text] [Related]
3. predPhogly-Site: Predicting phosphoglycerylation sites by incorporating probabilistic sequence-coupling information into PseAAC and addressing data imbalance.
Ahmed S; Rahman A; Hasan MAM; Islam MKB; Rahman J; Ahmad S
PLoS One; 2021; 16(4):e0249396. PubMed ID: 33793659
[TBL] [Abstract][Full Text] [Related]
4. PredNTS: Improved and Robust Prediction of Nitrotyrosine Sites by Integrating Multiple Sequence Features.
Nilamyani AN; Auliah FN; Moni MA; Shoombuatong W; Hasan MM; Kurata H
Int J Mol Sci; 2021 Mar; 22(5):. PubMed ID: 33800121
[TBL] [Abstract][Full Text] [Related]
5. NTyroSite: Computational Identification of Protein Nitrotyrosine Sites Using Sequence Evolutionary Features.
Hasan MM; Khatun MS; Mollah MNH; Yong C; Dianjing G
Molecules; 2018 Jul; 23(7):. PubMed ID: 29987232
[TBL] [Abstract][Full Text] [Related]
6. DeepPPSite: A deep learning-based model for analysis and prediction of phosphorylation sites using efficient sequence information.
Ahmed S; Kabir M; Arif M; Khan ZU; Yu DJ
Anal Biochem; 2021 Jan; 612():113955. PubMed ID: 32949607
[TBL] [Abstract][Full Text] [Related]
7. Accurately Predicting Glutarylation Sites Using Sequential Bi-Peptide-Based Evolutionary Features.
Arafat ME; Ahmad MW; Shovan SM; Dehzangi A; Dipta SR; Hasan MAM; Taherzadeh G; Shatabda S; Sharma A
Genes (Basel); 2020 Aug; 11(9):. PubMed ID: 32878321
[TBL] [Abstract][Full Text] [Related]
8. Computational Identification of Protein Pupylation Sites by Using Profile-Based Composition of k-Spaced Amino Acid Pairs.
Hasan MM; Zhou Y; Lu X; Li J; Song J; Zhang Z
PLoS One; 2015; 10(6):e0129635. PubMed ID: 26080082
[TBL] [Abstract][Full Text] [Related]
9. pNitro-Tyr-PseAAC: Predict Nitrotyrosine Sites in Proteins by Incorporating Five Features into Chou's General PseAAC.
Ghauri AW; Khan YD; Rasool N; Khan SA; Chou KC
Curr Pharm Des; 2018; 24(34):4034-4043. PubMed ID: 30479209
[TBL] [Abstract][Full Text] [Related]
10. DPP-PseAAC: A DNA-binding protein prediction model using Chou's general PseAAC.
Rahman MS; Shatabda S; Saha S; Kaykobad M; Rahman MS
J Theor Biol; 2018 Sep; 452():22-34. PubMed ID: 29753757
[TBL] [Abstract][Full Text] [Related]
11. Computational Prediction and Analysis for Tyrosine Post-Translational Modifications via Elastic Net.
Cao M; Chen G; Wang L; Wen P; Shi S
J Chem Inf Model; 2018 Jun; 58(6):1272-1281. PubMed ID: 29775287
[TBL] [Abstract][Full Text] [Related]
12. iLM-2L: A two-level predictor for identifying protein lysine methylation sites and their methylation degrees by incorporating K-gap amino acid pairs into Chou׳s general PseAAC.
Ju Z; Cao JZ; Gu H
J Theor Biol; 2015 Nov; 385():50-7. PubMed ID: 26254214
[TBL] [Abstract][Full Text] [Related]
13. PLP_FS: prediction of lysine phosphoglycerylation sites in protein using support vector machine and fusion of multiple F_Score feature selection.
Sohrawordi M; Hossain MA; Hasan MAM
Brief Bioinform; 2022 Sep; 23(5):. PubMed ID: 35929355
[TBL] [Abstract][Full Text] [Related]
14. Accurately predicting microbial phosphorylation sites using evolutionary and structural features.
Ahmed F; Dehzangi I; Hasan MM; Shatabda S
Gene; 2023 Jan; 851():146993. PubMed ID: 36272653
[TBL] [Abstract][Full Text] [Related]
15. Prediction of lysine propionylation sites using biased SVM and incorporating four different sequence features into Chou's PseAAC.
Ju Z; He JJ
J Mol Graph Model; 2017 Sep; 76():356-363. PubMed ID: 28763688
[TBL] [Abstract][Full Text] [Related]
16. SumSec: Accurate Prediction of Sumoylation Sites Using Predicted Secondary Structure.
Dehzangi A; López Y; Taherzadeh G; Sharma A; Tsunoda T
Molecules; 2018 Dec; 23(12):. PubMed ID: 30544729
[TBL] [Abstract][Full Text] [Related]
17. Mutli-Features Prediction of Protein Translational Modification Sites.
Bao W; Yuan CA; Zhang Y; Han K; Nandi AK; Honig B; Huang DS
IEEE/ACM Trans Comput Biol Bioinform; 2018; 15(5):1453-1460. PubMed ID: 28961121
[TBL] [Abstract][Full Text] [Related]
18. Bigram-PGK: phosphoglycerylation prediction using the technique of bigram probabilities of position specific scoring matrix.
Chandra A; Sharma A; Dehzangi A; Shigemizu D; Tsunoda T
BMC Mol Cell Biol; 2019 Dec; 20(Suppl 2):57. PubMed ID: 31856704
[TBL] [Abstract][Full Text] [Related]
19. A Systematic Review on Posttranslational Modification in Proteins: Feature Construction, Algorithm and Webserver.
Xu Y; Yang Y; Wang Z; Li C; Shao Y
Protein Pept Lett; 2018; 25(9):807-814. PubMed ID: 30255739
[TBL] [Abstract][Full Text] [Related]
20. Accurate in silico identification of protein succinylation sites using an iterative semi-supervised learning technique.
Zhao X; Ning Q; Chai H; Ma Z
J Theor Biol; 2015 Jun; 374():60-5. PubMed ID: 25843215
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
