176 related articles for article (PubMed ID: 35154264)
1. Identify DNA-Binding Proteins Through the Extreme Gradient Boosting Algorithm.
Zhao Z; Yang W; Zhai Y; Liang Y; Zhao Y
Front Genet; 2021; 12():821996. PubMed ID: 35154264
[TBL] [Abstract][Full Text] [Related]
2. HMMPred: Accurate Prediction of DNA-Binding Proteins Based on HMM Profiles and XGBoost Feature Selection.
Sang X; Xiao W; Zheng H; Yang Y; Liu T
Comput Math Methods Med; 2020; 2020():1384749. PubMed ID: 32300371
[TBL] [Abstract][Full Text] [Related]
3. FTWSVM-SR: DNA-Binding Proteins Identification via Fuzzy Twin Support Vector Machines on Self-Representation.
Zou Y; Ding Y; Peng L; Zou Q
Interdiscip Sci; 2022 Jun; 14(2):372-384. PubMed ID: 34743286
[TBL] [Abstract][Full Text] [Related]
4. Prediction of hot spots in protein-DNA binding interfaces based on supervised isometric feature mapping and extreme gradient boosting.
Li K; Zhang S; Yan D; Bin Y; Xia J
BMC Bioinformatics; 2020 Sep; 21(Suppl 13):381. PubMed ID: 32938395
[TBL] [Abstract][Full Text] [Related]
5. DP-BINDER: machine learning model for prediction of DNA-binding proteins by fusing evolutionary and physicochemical information.
Ali F; Ahmed S; Swati ZNK; Akbar S
J Comput Aided Mol Des; 2019 Jul; 33(7):645-658. PubMed ID: 31123959
[TBL] [Abstract][Full Text] [Related]
6. DBPMod: a supervised learning model for computational recognition of DNA-binding proteins in model organisms.
Pradhan UK; Meher PK; Naha S; Sharma NK; Agarwal A; Gupta A; Parsad R
Brief Funct Genomics; 2023 Aug; ():. PubMed ID: 37651627
[TBL] [Abstract][Full Text] [Related]
7. HKAM-MKM: A hybrid kernel alignment maximization-based multiple kernel model for identifying DNA-binding proteins.
Zhao S; Ding Y; Liu X; Su X
Comput Biol Med; 2022 Jun; 145():105395. PubMed ID: 35334314
[TBL] [Abstract][Full Text] [Related]
8. PDRLGB: precise DNA-binding residue prediction using a light gradient boosting machine.
Deng L; Pan J; Xu X; Yang W; Liu C; Liu H
BMC Bioinformatics; 2018 Dec; 19(Suppl 19):522. PubMed ID: 30598073
[TBL] [Abstract][Full Text] [Related]
9. A sequence-based multiple kernel model for identifying DNA-binding proteins.
Qian Y; Jiang L; Ding Y; Tang J; Guo F
BMC Bioinformatics; 2021 May; 22(Suppl 3):291. PubMed ID: 34058979
[TBL] [Abstract][Full Text] [Related]
10. MV-H-RKM: A Multiple View-Based Hypergraph Regularized Restricted Kernel Machine for Predicting DNA-Binding Proteins.
Guan S; Qian Y; Jiang T; Jiang M; Ding Y; Wu H
IEEE/ACM Trans Comput Biol Bioinform; 2023; 20(2):1246-1256. PubMed ID: 35731758
[TBL] [Abstract][Full Text] [Related]
11. PredDBP-Stack: Prediction of DNA-Binding Proteins from HMM Profiles using a Stacked Ensemble Method.
Wang J; Zheng H; Yang Y; Xiao W; Liu T
Biomed Res Int; 2020; 2020():7297631. PubMed ID: 32352006
[TBL] [Abstract][Full Text] [Related]
12. On the Use of Machine Learning Models for Prediction of Compressive Strength of Concrete: Influence of Dimensionality Reduction on the Model Performance.
Wan Z; Xu Y; Šavija B
Materials (Basel); 2021 Feb; 14(4):. PubMed ID: 33546376
[TBL] [Abstract][Full Text] [Related]
13. SubMito-XGBoost: predicting protein submitochondrial localization by fusing multiple feature information and eXtreme gradient boosting.
Yu B; Qiu W; Chen C; Ma A; Jiang J; Zhou H; Ma Q
Bioinformatics; 2020 Feb; 36(4):1074-1081. PubMed ID: 31603468
[TBL] [Abstract][Full Text] [Related]
14. BiCaps-DBP: Predicting DNA-binding proteins from protein sequences using Bi-LSTM and a 1D-capsule network.
Mursalim MKN; Mengko TLER; Hertadi R; Purwarianti A; Susanty M
Comput Biol Med; 2023 Sep; 163():107241. PubMed ID: 37437362
[TBL] [Abstract][Full Text] [Related]
15. Intelligent Fault Diagnosis of Diesel Engines via Extreme Gradient Boosting and High-Accuracy Time-Frequency Information of Vibration Signals.
Tao J; Qin C; Li W; Liu C
Sensors (Basel); 2019 Jul; 19(15):. PubMed ID: 31349707
[TBL] [Abstract][Full Text] [Related]
16. FKRR-MVSF: A Fuzzy Kernel Ridge Regression Model for Identifying DNA-Binding Proteins by Multi-View Sequence Features via Chou's Five-Step Rule.
Zou Y; Ding Y; Tang J; Guo F; Peng L
Int J Mol Sci; 2019 Aug; 20(17):. PubMed ID: 31454964
[TBL] [Abstract][Full Text] [Related]
17. Prediction of protein-protein interaction sites through eXtreme gradient boosting with kernel principal component analysis.
Wang X; Zhang Y; Yu B; Salhi A; Chen R; Wang L; Liu Z
Comput Biol Med; 2021 Jul; 134():104516. PubMed ID: 34119922
[TBL] [Abstract][Full Text] [Related]
18. iT3SE-PX: Identification of Bacterial Type III Secreted Effectors Using PSSM Profiles and XGBoost Feature Selection.
Ding C; Han H; Li Q; Yang X; Liu T
Comput Math Methods Med; 2021; 2021():6690299. PubMed ID: 33505516
[TBL] [Abstract][Full Text] [Related]
19. Diagnostic classification of cancers using extreme gradient boosting algorithm and multi-omics data.
Ma B; Meng F; Yan G; Yan H; Chai B; Song F
Comput Biol Med; 2020 Jun; 121():103761. PubMed ID: 32339094
[TBL] [Abstract][Full Text] [Related]
20. XGBPRH: Prediction of Binding Hot Spots at Protein⁻RNA Interfaces Utilizing Extreme Gradient Boosting.
Deng L; Sui Y; Zhang J
Genes (Basel); 2019 Mar; 10(3):. PubMed ID: 30901953
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]