These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
155 related articles for article (PubMed ID: 25409550)
1. Sequence-based identification of recombination spots using pseudo nucleic acid representation and recursive feature extraction by linear kernel SVM. Li L; Yu S; Xiao W; Li Y; Huang L; Zheng X; Zhou S; Yang H BMC Bioinformatics; 2014 Nov; 15(1):340. PubMed ID: 25409550 [TBL] [Abstract][Full Text] [Related]
2. Combining pseudo dinucleotide composition with the Z curve method to improve the accuracy of predicting DNA elements: a case study in recombination spots. Dong C; Yuan YZ; Zhang FZ; Hua HL; Ye YN; Labena AA; Lin H; Chen W; Guo FB Mol Biosyst; 2016 Aug; 12(9):2893-900. PubMed ID: 27410247 [TBL] [Abstract][Full Text] [Related]
3. Recombination spot identification Based on gapped k-mers. Wang R; Xu Y; Liu B Sci Rep; 2016 Mar; 6():23934. PubMed ID: 27030570 [TBL] [Abstract][Full Text] [Related]
5. iRSpot-ADPM: Identify recombination spots by incorporating the associated dinucleotide product model into Chou's pseudo components. Zhang L; Kong L J Theor Biol; 2018 Mar; 441():1-8. PubMed ID: 29305179 [TBL] [Abstract][Full Text] [Related]
6. iRSpot-PDI: Identification of recombination spots by incorporating dinucleotide property diversity information into Chou's pseudo components. Zhang L; Kong L Genomics; 2019 May; 111(3):457-464. PubMed ID: 29548799 [TBL] [Abstract][Full Text] [Related]
7. 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]
8. iRSpot-DTS: Predict recombination spots by incorporating the dinucleotide-based spare-cross covariance information into Chou's pseudo components. Zhang S; Yang K; Lei Y; Song K Genomics; 2019 Dec; 111(6):1760-1770. PubMed ID: 30529702 [TBL] [Abstract][Full Text] [Related]
9. Recombination Hotspot/Coldspot Identification Combining Three Different Pseudocomponents via an Ensemble Learning Approach. Liu B; Liu Y; Huang D Biomed Res Int; 2016; 2016():8527435. PubMed ID: 27648451 [TBL] [Abstract][Full Text] [Related]
10. iRSpot-Pse6NC: Identifying recombination spots in Yang H; Qiu WR; Liu G; Guo FB; Chen W; Chou KC; Lin H Int J Biol Sci; 2018; 14(8):883-891. PubMed ID: 29989083 [TBL] [Abstract][Full Text] [Related]
11. Prediction of Recombination Spots Using Novel Hybrid Feature Extraction Method via Deep Learning Approach. Khan F; Khan M; Iqbal N; Khan S; Muhammad Khan D; Khan A; Wei DQ Front Genet; 2020; 11():539227. PubMed ID: 33093842 [TBL] [Abstract][Full Text] [Related]
12. iDNAProt-ES: Identification of DNA-binding Proteins Using Evolutionary and Structural Features. Chowdhury SY; Shatabda S; Dehzangi A Sci Rep; 2017 Nov; 7(1):14938. PubMed ID: 29097781 [TBL] [Abstract][Full Text] [Related]
13. A feature-based approach to predict hot spots in protein-DNA binding interfaces. Zhang S; Zhao L; Zheng CH; Xia J Brief Bioinform; 2020 May; 21(3):1038-1046. PubMed ID: 30957840 [TBL] [Abstract][Full Text] [Related]
14. 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]
15. Protein submitochondrial localization from integrated sequence representation and SVM-based backward feature extraction. Li L; Yu S; Xiao W; Li Y; Hu W; Huang L; Zheng X; Zhou S; Yang H Mol Biosyst; 2015 Jan; 11(1):170-7. PubMed ID: 25335193 [TBL] [Abstract][Full Text] [Related]
16. iTIS-PseKNC: Identification of Translation Initiation Site in human genes using pseudo k-tuple nucleotides composition. Kabir M; Iqbal M; Ahmad S; Hayat M Comput Biol Med; 2015 Nov; 66():252-7. PubMed ID: 26433457 [TBL] [Abstract][Full Text] [Related]
17. SVM-RFE: selection and visualization of the most relevant features through non-linear kernels. Sanz H; Valim C; Vegas E; Oller JM; Reverter F BMC Bioinformatics; 2018 Nov; 19(1):432. PubMed ID: 30453885 [TBL] [Abstract][Full Text] [Related]
18. iRecSpot-EF: Effective sequence based features for recombination hotspot prediction. Jani MR; Khan Mozlish MT; Ahmed S; Tahniat NS; Farid DM; Shatabda S Comput Biol Med; 2018 Dec; 103():17-23. PubMed ID: 30336361 [TBL] [Abstract][Full Text] [Related]
19. RF-DYMHC: detecting the yeast meiotic recombination hotspots and coldspots by random forest model using gapped dinucleotide composition features. Jiang P; Wu H; Wei J; Sang F; Sun X; Lu Z Nucleic Acids Res; 2007 Jul; 35(Web Server issue):W47-51. PubMed ID: 17478517 [TBL] [Abstract][Full Text] [Related]
20. PseDNA-Pro: DNA-Binding Protein Identification by Combining Chou's PseAAC and Physicochemical Distance Transformation. Liu B; Xu J; Fan S; Xu R; Zhou J; Wang X Mol Inform; 2015 Jan; 34(1):8-17. PubMed ID: 27490858 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]