322 related articles for article (PubMed ID: 33093842)
1. 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]
2. 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]
3. 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]
4. 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]
5. Sequence-dependent prediction of recombination hotspots in Saccharomyces cerevisiae.
Liu G; Liu J; Cui X; Cai L
J Theor Biol; 2012 Jan; 293():49-54. PubMed ID: 22016025
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. Recognition of Consumer Preference by Analysis and Classification EEG Signals.
Aldayel M; Ykhlef M; Al-Nafjan A
Front Hum Neurosci; 2020; 14():604639. PubMed ID: 33519402
[TBL] [Abstract][Full Text] [Related]
8. CoAID-DEEP: An Optimized Intelligent Framework for Automated Detecting COVID-19 Misleading Information on Twitter.
Abdelminaam DS; Ismail FH; Taha M; Taha A; Houssein EH; Nabil A
IEEE Access; 2021; 9():27840-27867. PubMed ID: 34786308
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. 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]
11. 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]
12. 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]
13. 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]
14. SVM-RLF-DNN: A DNN with reliefF and SVM for automatic identification of COVID from chest X-ray and CT images.
Saha S; Nandi D
Digit Health; 2024; 10():20552076241257045. PubMed ID: 38812845
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. 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]
17. 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]
18. Enhanced prediction of recombination hotspots using input features extracted by class specific autoencoders.
Nath A; Karthikeyan S
J Theor Biol; 2018 May; 444():73-82. PubMed ID: 29462625
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. Support vector machine for classification of meiotic recombination hotspots and coldspots in Saccharomyces cerevisiae based on codon composition.
Zhou T; Weng J; Sun X; Lu Z
BMC Bioinformatics; 2006 Apr; 7():223. PubMed ID: 16640774
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
