114 related articles for article (PubMed ID: 30529702)
1. 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]
2. 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]
3. 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]
4. 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]
5. iRSpot-GAEnsC: identifing recombination spots via ensemble classifier and extending the concept of Chou's PseAAC to formulate DNA samples.
Kabir M; Hayat M
Mol Genet Genomics; 2016 Feb; 291(1):285-96. PubMed ID: 26319782
[TBL] [Abstract][Full Text] [Related]
6. iRSpot-DACC: a computational predictor for recombination hot/cold spots identification based on dinucleotide-based auto-cross covariance.
Liu B; Liu Y; Jin X; Wang X; Liu B
Sci Rep; 2016 Sep; 6():33483. PubMed ID: 27641752
[TBL] [Abstract][Full Text] [Related]
7. iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition.
Chen W; Feng PM; Lin H; Chou KC
Nucleic Acids Res; 2013 Apr; 41(6):e68. PubMed ID: 23303794
[TBL] [Abstract][Full Text] [Related]
8. iRSpot-SF: Prediction of recombination hotspots by incorporating sequence based features into Chou's Pseudo components.
Al Maruf MA; Shatabda S
Genomics; 2019 Jul; 111(4):966-972. PubMed ID: 29935224
[TBL] [Abstract][Full Text] [Related]
9. iRSpot-TNCPseAAC: identify recombination spots with trinucleotide composition and pseudo amino acid components.
Qiu WR; Xiao X; Chou KC
Int J Mol Sci; 2014 Jan; 15(2):1746-66. PubMed ID: 24469313
[TBL] [Abstract][Full Text] [Related]
10. iRSpot-EL: identify recombination spots with an ensemble learning approach.
Liu B; Wang S; Long R; Chou KC
Bioinformatics; 2017 Jan; 33(1):35-41. PubMed ID: 27531102
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. 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]
13. iDHS-DSAMS: Identifying DNase I hypersensitive sites based on the dinucleotide property matrix and ensemble bagged tree.
Zhang S; Yu Q; He H; Zhu F; Wu P; Gu L; Jiang S
Genomics; 2020 Mar; 112(2):1282-1289. PubMed ID: 31377426
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. 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]
16. 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]
17. Using weighted features to predict recombination hotspots in Saccharomyces cerevisiae.
Liu G; Xing Y; Cai L
J Theor Biol; 2015 Oct; 382():15-22. PubMed ID: 26141645
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. A comparison and assessment of computational method for identifying recombination hotspots in Saccharomyces cerevisiae.
Yang H; Yang W; Dao FY; Lv H; Ding H; Chen W; Lin H
Brief Bioinform; 2020 Sep; 21(5):1568-1580. PubMed ID: 31633777
[TBL] [Abstract][Full Text] [Related]
20. Identify DNA-binding proteins with optimal Chou's amino acid composition.
Zhao XW; Li XT; Ma ZQ; Yin MH
Protein Pept Lett; 2012 Apr; 19(4):398-405. PubMed ID: 22316304
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]