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2020 – today
- 2021
[b2]Zheng Rong Yang:
Biological Pattern Discovery with R - Machine Learning Approaches. WorldScientific 2021, ISBN 9789811240119, pp. 1-464
2010 – 2019
- 2014
- [c26]ZiHua Yang, Abdullatif Alwatban, Zheng Rong Yang:
A mean pattern model for integrative study - Integrative self-organizing map. BMEI 2014: 643-648 - [c25]Louisa Knocker, Zheng Rong Yang:
SLC- and NDUF-genes expression dynamics in pre-implantation embryonic development between bovine and mouse - A bioinformatics study. BMEI 2014: 665-670 - [c24]Suhaib Mohammed, Ozgur E. Akman, Zheng Rong Yang:
A consensus approach to predict regulatory interactions. BMEI 2014: 769-775 - [c23]Amelia King, ZiHua Yang, Zheng Rong Yang:
Multivariate multi-scale Gaussian for microarray unsupervised classification. IJCNN 2014: 2458-2463 - 2012
- [j32]Zhijun Yang, Zheng Rong Yang, Trygve Eftestøl, Petter Andreas Steen, Weiping Lu, Robert G. Harrison:
A Mixture Model Classifier and its Application on the Biomedical Time Series. Appl. Artif. Intell. 26(6): 588-597 (2012) - 2010
- [b1]Zheng Rong Yang:
Machine Learning Approaches to Bioinformatics. Science, Engineering, and Biology Informatics 4, World Scientific 2010, ISBN 978-981-4287-30-2, pp. 1-336
2000 – 2009
- 2009
- [j31]Zheng Rong Yang, Ganjana Lertmemongkolchai
, Gladys Tan, Philip L. Felgner, Richard W. Titball:
A genetic programming approach for Burkholderia Pseudomallei diagnostic pattern discovery. Bioinform. 25(17): 2256-2262 (2009) - [j30]Zheng Rong Yang:
Predict prokaryotic proteins through detecting N-formylmethionine residues in protein sequences using support vector machine. Biosyst. 97(3): 141-145 (2009) - [j29]Zheng Rong Yang:
Predicting sulfotyrosine sites using the random forest algorithm with significantly improved prediction accuracy. BMC Bioinform. 10: 361 (2009) - [j28]Zheng Rong Yang:
Predict Collagen Hydroxyproline Sites Using Support Vector Machines. J. Comput. Biol. 16(5): 691-702 (2009) - [p1]Zheng Rong Yang:
Peptide Bioinformatics- Peptide Classification Using Peptide Machines. Artificial Neural Networks 2009: 155-179 - 2008
- [c22]Zheng Rong Yang:
Crosstalk and Signalling Pathway Complexity - A Case Study on Synthetic Models. HAIS 2008: 696-705 - [c21]Zheng Rong Yang:
Explore Residue Significance in Peptide Classification. HAIS 2008: 706-713 - [c20]Zheng Rong Yang:
Single-Layer Neural Net Competes with Multi-layer Neural Net. IDEAL 2008: 516-524 - 2007
- [j27]Zheng Rong Yang:
A Probabilistic Peptide Machine for Predicting Hepatitis C Virus Protease Cleavage Sites. IEEE Trans. Inf. Technol. Biomed. 11(5): 593-595 (2007) - [c19]David C. Trudgian, Zheng Rong Yang:
Substitution Matrix Optimisation for Peptide Classification. EvoBIO 2007: 291-300 - [c18]David C. Trudgian, Zheng Rong Yang:
A Sparse Bayesian Position Weighted Bio-Kernel Network. IDEAL 2007: 527-536 - [c17]Zheng Rong Yang:
Peptide Classification with Genetic Programming Ensemble of Generalised Indicator Models. IMECS 2007: 319-324 - [c16]Zheng Rong Yang:
Predicting Palmitoylation Sites Using a Regularised Bio-basis Function Neural Network. ISBRA 2007: 406-417 - 2006
- [j26]Zheng Rong Yang, Jonathan Dry, Rebecca Thomson, T. Charles Hodgman:
A bio-basis function neural network for protein peptide cleavage activity characterisation. Neural Networks 19(4): 401-407 (2006) - [j25]Zheng Rong Yang:
Predicting Hepatitis C Virus Protease Cleavage Sites Using Generalized Linear Indicator Regression Models. IEEE Trans. Biomed. Eng. 53(10): 2119-2123 (2006) - [j24]Zheng Rong Yang:
A novel radial basis function neural network for discriminant analysis. IEEE Trans. Neural Networks 17(3): 604-612 (2006) - [c15]Zheng Rong Yang:
A Fast Algorithm for Relevance Vector Machine. IDEAL 2006: 33-39 - [c14]Natasha Young, Zheng Rong Yang:
Multivariate Crosstalk Models. IDEAL 2006: 1129-1136 - [c13]Alex C. Thomas, Zheng Rong Yang:
Improved Prediction of HIV-1 Protease Genotypic Resistance Testing Assays using a Consensus Technique. IJCNN 2006: 2308-2314 - [c12]David C. Trudgian, Felicia Charles Johnson, Zheng Rong Yang:
Predicting HIV-1 T Cell Epitopes Using Bio-basis Function Neural Networks. IJCNN 2006: 3586-3593 - 2005
- [j23]Zheng Rong Yang:
Prediction of caspase cleavage sites using Bayesian bio-basis function neural networks. Bioinform. 21(9): 1831-1837 (2005) - [j22]Zheng Rong Yang:
Mining SARS-CoV protease cleavage data using non-orthogonal decision trees: a novel method for decisive template selection. Bioinform. 21(11): 2644-2650 (2005) - [j21]Zheng Rong Yang, Rebecca Thomson, Philip McNeil, Robert Esnouf:
RONN: the bio-basis function neural network technique applied to the detection of natively disordered regions in proteins. Bioinform. 21(16): 3369-3376 (2005) - [j20]Pasak Senawongse, Andrew R. Dalby, Zheng Rong Yang:
Predicting the Phosphorylation Sites Using Hidden Markov Models and Machine Learning Methods. J. Chem. Inf. Model. 45(4): 1147-1152 (2005) - [j19]Zheng Rong Yang, Felicia Charles Johnson:
Prediction of T-Cell Epitopes Using Biosupport Vector Machines. J. Chem. Inf. Model. 45(5): 1424-1428 (2005) - [j18]Zheng Rong Yang, Rebecca Thomson:
Bio-basis function neural network for prediction of protease cleavage sites in proteins. IEEE Trans. Neural Networks 16(1): 263-274 (2005) - [j17]Zheng Rong Yang:
Orthogonal kernel Machine for the prediction of functional sites in proteins. IEEE Trans. Syst. Man Cybern. Part B 35(1): 100-106 (2005) - [c11]Zheng Rong Yang, Natasha Young:
Bio-kernel Self-organizing Map for HIV Drug Resistance Classification. ICNC (1) 2005: 179-186 - [c10]Zheng Rong Yang:
Bayesian Radial Basis Function Neural Network. IDEAL 2005: 211-219 - 2004
- [j16]Zheng Rong Yang:
Biological applications of support vector machines. Briefings Bioinform. 5(4): 328-338 (2004) - [j15]Zheng Rong Yang, Kuo-Chen Chou:
Bio-support vector machines for computational proteomics. Bioinform. 20(5): 735-741 (2004) - [j14]Zheng Rong Yang, Kuo-Chen Chou:
Predicting the linkage sites in glycoproteins using bio-basis function neural network. Bioinform. 20(6): 903-908 (2004) - [j13]Zheng Rong Yang:
Mining gene expression data based on template theory. Bioinform. 20(16): 2759-2766 (2004) - [j12]Zheng Rong Yang, Andrew R. Dalby, Jing Qiu:
Mining HIV protease cleavage data using genetic programming with a sum-product function. Bioinform. 20(18): 3398-3405 (2004) - [j11]Emily A. Berry, Andrew R. Dalby, Zheng Rong Yang:
Reduced bio basis function neural network for identification of protein phosphorylation sites: comparison with pattern recognition algorithms. Comput. Biol. Chem. 28(1): 75-85 (2004) - [j10]Zheng Rong Yang, Emily A. Berry:
Reduced Bio-basis Function Neural Networks for Protease Cleavage Site Prediction. J. Bioinform. Comput. Biol. 2(3): 511-532 (2004) - [j9]Vishwesh Venkatraman, Andrew R. Dalby, Zheng Rong Yang:
Evaluation of Mutual Information and Genetic Programming for Feature Selection in QSAR. J. Chem. Inf. Model. 44(5): 1686-1692 (2004) - [e1]Zheng Rong Yang, Richard M. Everson, Hujun Yin:
Intelligent Data Engineering and Automated Learning - IDEAL 2004, 5th International Conference, Exeter, UK, August 25-27, 2004, Proceedings. Lecture Notes in Computer Science 3177, Springer 2004, ISBN 3-540-22881-0 [contents] - 2003
- [j8]Rebecca Thomson, T. Charles Hodgman, Zheng Rong Yang, Austin K. Doyle:
Characterizing proteolytic cleavage site activity using bio-basis function neural networks. Bioinform. 19(14): 1741-1747 (2003) - [j7]Zheng Rong Yang, Kuo-Chen Chou:
Mining Biological Data Using Self-Organizing Map. J. Chem. Inf. Comput. Sci. 43(6): 1748-1753 (2003) - [j6]Zheng Rong Yang, Robert G. Harrison:
An unsupervised probabilistic net for health inequalities analysis. IEEE Trans. Neural Networks 14(1): 46-57 (2003) - [c9]Zheng Rong Yang:
Support vector machines for company failure prediction. CIFEr 2003: 47-54 - [c8]Emily A. Berry, Zheng Rong Yang, Xikun Wu:
A Biology Inspired Neural Learning Algorithm for Analysing Protein Sequence. ICTAI 2003: 18-25 - 2002
- [j5]Zheng Rong Yang, Robert G. Harrison:
Analysing company performance using templates. Intell. Data Anal. 6(1): 3-15 (2002) - [c7]Ajit Narayanan, Xikun Wu, Zheng Rong Yang:
Mining viral protease data to extract cleavage knowledge. ISMB 2002: 5-13 - 2001
- [j4]Zheng Rong Yang, Weiping Lu, Robert G. Harrison:
Evolving Stacked Time Series Predictors with Multiple Window Scales and Sampling Gaps. Neural Process. Lett. 13(3): 203-211 (2001) - [j3]Zheng Rong Yang, Mark Zwolinski:
Mutual Information Theory for Adaptive Mixture Models. IEEE Trans. Pattern Anal. Mach. Intell. 23(4): 396-403 (2001) - [c6]Zheng Rong Yang:
Analysing Health Inequalities Using SOM. WSOM 2001: 47-53 - 2000
- [j2]Zheng Rong Yang, Mark Zwolinski, Chris D. Chalk, Alan Christopher Williams:
Applying a robust heteroscedastic probabilistic neural network toanalog fault detection and classification. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 19(1): 142-151 (2000) - [c5]Zheng Rong Yang, Mark Zwolinski:
Applying Mutual Information to Adaptive Mixture Models. IDEAL 2000: 250-255 - [c4]Zheng Rong Yang:
Stability Analysis of Financial Ratios. IDEAL 2000: 256-261 - [c3]Zheng Rong Yang, Robert G. Harrison, Weiping Lu:
Identifying Health Inequalities Using Artificial Neural Networks (WHO Data). IJCNN (4) 2000: 113-118 - [c2]Zheng Rong Yang, Weiping Lu, Dejin Yu, Robert G. Harrison:
Detecting False Benign in Breast Cancer Diagnosis. IJCNN (3) 2000: 655-658
1990 – 1999
- 1999
- [c1]Zheng Rong Yang, Mark Zwolinski:
Fast, Robust DC and Transient Fault Simulation for Nonlinear Analog Circuits. DATE 1999: 244-248 - 1998
- [j1]Zheng Rong Yang, Sheng Chen:
Robust maximum likelihood training of heteroscedastic probabilistic neural networks. Neural Networks 11(4): 739-747 (1998)
Coauthor Index
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