Qualifications: BSc (East China Normal University) MIT (Griffith University, Australia) PhD (Bond University, Australia) Dr. Ping Zhang had 12 years work experience from Yantai Electronic Institute, China before her Masters and Ph.D study in Australia. She was named as a top-three finalist in the Queensland Government’s Smart Women – Smart State Awards for recognizing her PhD work in computer aided diagnosis for breast cancer in digital mammography. The diagnosis algorithm developed by her is being patented. Prior to joining the Faculty of Health Sciences and Medicine at Bond University, she worked as a research officer and senior research officer at the Institute for Molecular Bioscience and Diamantina Institute for Cancer, Immunology and Metabolic Medicine at The University of Queensland from 2006 to 2009. She worked on a FP6 (European Union funded, Frame Program 6) project on simulating the human immune system in computers, and collaborated with medical researchers with bioinformatics and computational expertise. Her research interests include pattern recognition, applying machine learning and statistical techniques for medical decision making and bioinformatics.
Articles
Clinical diagnostic criteria for isolating patients admitted to hospital with suspected pandemic influenza (with John Gerrard, Gerben Keijzers, Caleb Vossen, and Deborough Macbeth), The lancet (2009)
Extract: Australian hospitals have now experienced the first wave of pandemic H1N1 influenza during a...
Searching of optimal vaccination schedules: Application of genetic algorithms to approach the problem in cancer immunoprevention (with Marzio Alfio Pennisi, Francesco Pappalardo, and Santo Motta), IEEE Engineering in medicine and biology magazine (2009)
Genetic algorithms (GAs) are a particular class of evolutionary algorithms that use techniques inspired by...
Computational simulations of the immune system for personalized medicine: State of the art and challenges (with F. Pappalardo, P. Zhang, M. Halling-Brown, K. Basford, A. Scalia, A. Shepherd, D. Moss, S. Motta, and V. Brusic), Current pharmacogenomics and personalized medicine (2008)
The main goal of pharmacogenomics is to study the effects of genetic variation on patient...
Neural vs. statistical classifier in conjunction with genetic algorithm based feature selection (with Brijesh Verma and Kuldeep Kumar), Pattern recognition letters (2005)
Digital mammography is one of the most suitable methods for early detection of breast cancer....
Book Chapters
A Hybrid Approach Based on Genetic Algorithms in Conjunction with Statistical Methods for the Diagnosis of Breast Cancer (with Brijesh Verma and Kuldeep Kumar), Advanced Computational Methods for Biocomputing and Bioimaging (2006)
This chapter reviews and presents genetic algorithms and statistical methods based approach for early detection...
Conference Papers
A hybrid model for prediction of peptide binding to MHC molecules (with P. Zhang, V. Brusic, and K. Basford), Paper presented at the 15th International Conference on Neuro-Information Processing (ICONIP 2008) (2008)
We propose a hybrid classification system for predicting peptide binding to major histocompatibility complex (MHC)...
Understanding prediction systems for HLA-binding peptides and T-cell epitope identification (with L. You, P. Zhang, M. Boden, and V. Brusic), Paper presented at the International workshop on pattern recognition in bioinformatics (2007)
Peptide binding to HLA molecules is a critical step in induction and regulation of T-cell...
Analysing Feature Significance from Various Systems for Mass Diagnosis (with Kuldeep Kumar), International Conference on Computational Intelligence for Modelling, Control and Automation - CIMCA06 Jointly with International Conference on Intelligent Agents, Web Technologies and Internet Commerce - IAWTIC06 (2006)
This paper compares a few classification models for mass classification and analyzes the feature significance...
Application of Decision Trees for Mass Classification in Mammography (with Kuldeep Kumar and Brijesh Verma), The 2nd International Conference on Natural Computation and the 3rd International Conference on Fuzzy Systems and Knowledge Discovery (2006)
This paper discusses the effectiveness of using decision trees for mass classification in mammography. The...
A hybrid classifier for mass classification with different kinds of features in mammography (with Kuldeep Kumar and Brijesh Verma), Paper presented at the 2nd international conference on Fuzzy Systems and Knowledge Discovery (FSKD 2005) (2005)
This paper proposes a hybrid system which combines computer extracted features and human interpreted features...