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Publications:

Chen, M., Stitt, H., Chu, R., Kistemaker, J.C.M., Wood, I.A., Burn, P.L., Gentle, I.R. and Shaw, P.E. Naphthalimide Derivatives as Film-Based Fluorescent Sensors for Rapid Detection of Illicit Drugs, Advanced Sensor Research, e00063, 2025.

Feraud, M., O'Brien, J.W., Samanipour, S., Dewapriya, P, van Herwerden, D., Kaserzon, S., Wood, I., Rauert, C. and Thomas, K.V. InSpectra-A platform for identifying emerging chemical threats, Journal of Hazardous Materials, 455, 131486, 21, 2023.

Zheng, C., Huang, J., Wood, I.A. and Wu, Y. A modified expectation-maximization algorithm for latent Gaussian graphical model, Canadian Journal of Statistics, 50 (2), 612-637, 2022.

Bassett, J.J., Robitaille, M., Peters, A.A., Bong, A.H.L., Taing, M.-W., Wood, I.A., Sadras, F., Roberts-Thomson, S.J. and Monteith, G.R. ORAI1 regulates sustained cytosolic free calcium fluctuations during breast cancer cell apoptosis and apoptotic resistance via a STIM1 independent pathway, The FASEB Journal, 36 (1), e22108,15, 2022.

Van Ryt, S., Gallagher, M. and Wood, I. A Novel Mutation Operator for Variable Length Algorithms, Australasian Joint Conference on Artificial Intelligence, 176-188, 2020.

Saleem, S., Gallagher, M. and Wood, I. Direct feature evaluation in black-box optimization using problem transformations, Evolutionary Computation, 27 (1), 75-98, 2019.

Saleem, S., Gallagher, M. and Wood, I. A model-based framework for black-box problem comparison using Gaussian processes, 15th International Conference on Parallel Problem Solving from Nature, 284-295, 2018.

Bogomolov, T., Filar, J.A., Luscombe, R., Nazarathy, Y., Qin, S., Swierkowski, P. and Wood, I. Size does matter: a simulation study of hospital size and operational efficiency, 22nd International Congress on Modelling and Simulation, 1274-1280, 2017.

Nguyen, H.D. and Wood, I.A. A Block Successive Lower-Bound Maximization Algorithm for the Maximum Pseudo-Likelihood Estimation of Fully Visible Boltzmann Machines, Neural Computation, 28, 485-492, 2016.

Nguyen, H.D. and Wood, I.A. Asymptotic Normality of the Maximum Pseudolikelihood Estimator for Fully Visible Boltzmann Machines, IEEE Transactions on Neural Networks and Learning Systems, 27, 897-902, 2016.

Nguyen, H.D., McLachlan, G.J. and Wood, I.A. Mixtures of Spatial Spline Regressions for Clustering and Classification, Computational Statistics and Data Analysis, 93, 76-85, 2016.

Ahfock, D., Wood, I., Stephen, S., Cavanagh, C.R. and Huang, B.E. Characterizing Uncertainty in High-Density Maps from Multiparental Populations, Genetics 198, 117-128, 2014.

Ormerod, K.L., Byrnes III, E.J., Wood, I.A., Lodge, J.K., Heitman, J.and Fraser, J.A. The H99 family tree: variation in the common laboratory strains of Cryptococcus neoformans var. grubii characterised through whole-genome sequencing, Mycoses 57, 69-70, 2014.

Janbon, G., Ormerod, K.L., Paulet, D., Byrnes, E.J. III, Yadav, V. et al. Analysis of the Genome and Transcriptome of Cryptococcus neoformans var. grubii Reveals Complex RNA Expression and Microevolution Leading to Virulence Attenuation, PloS Genetics 10: e1004261, 2014.

Gao, B. and Wood, I.A. TAM-EDA: Multivariate t Distribution, Archive and Mutation Based Estimation of Distribution Algorithm, ANZIAM Journal, 54 C720-C746, 2014.

Gollapalli, M., Li, X. and Wood, I. Automated discovery of multi-faceted ontologies for accurate query answering and future semantic reasoning, Data & Knowledge Engineering 87 405–424, 2013.

Nguyen, H.D., Wood, I.A. and Hill, M.M. A robust permutation test for quantitative SILAC proteomics experiments, Journal of Integrated OMICS 2 80-93, 2012.

Grace, A.W. and Wood, I.A., Approximating the Tail of the Anderson-Darling Distribution, Computational Statistics and Data Analysis, 56 4301–4311, 2012.

Nguyen, H.D. and Wood, I.A., Variable selection in statistical models using population-based incremental learning with applications to genome-wide association studies, Proceedings of the IEEE Congress on Evolutionary Computation, 1841-1848, IEEE Press, 2012.

Chow, E.W.L., Morrow, C.A., Djordjevic, J.T., Wood, I.A. and Fraser, J.A. Microevolution of Cryptococcus neoformans Driven by Massive Tandem Gene Amplification, Molecular Biology and Evolution, 29 1987-2000, 2012.

Gollapalli, M. Li, X., Wood, I. and Governatori, G., Ontology Guided Data Linkage Framework for Discovering Meaningful Data Facts, Proceedings of the International Conference on Advanced Data Mining and Applications (ADMA 2011), Part II, Lecture Notes in Artificial Intelligence 7121 252-265, 2011.

Gollapalli, M. Li, X., Wood, I. and Governatori, G., Approximate Record Matching Using Hash Grams, Proceedings of the 11th IEEE International Conference on Data Mining Workshops (ICDM 2011) 504-511, 2011.

Rathnayake, S.I., Wood, I.A., Abeyratne, U.R. and Hukins, C., Nonlinear features for single channel diagnosis of sleep disordered breathing diseases, IEEE Transactions on Biomedical Engineering 57 1973-1981, 2010.

Oldmeadow, C., Wood, I., Mengersen, K., Visscher, P.M., Martin, N.G. and Duffy, D.L., Investigation of the relationship between smoking and appendicitis in Australian twins, Annals of Epidemiology, 18 631-636, 2008.

 Zhu, J.X., McLachlan, G.J., Ben-Tovim Jones, L. and Wood, I.A. On Selection Biases with Prediction Rules Formed from Gene Expression Data, Journal of Statistical Planning and Inference, 138 374-386, 2008.

Gallagher, M., Wood, I., Keith, J. and Sofronov, G.  Bayesian Inference in Estimation of Distribution Algorithms, In Proceedings of the IEEE Congress on Evolutionary Computation, 127-133, Singapore, IEEE Press, 2007.

Wood, I.A., Visscher, P.M. and Mengersen, K.L., Classification based upon gene expression data: bias and precision of error rates, Bioinformatics 23 1363-1370, 2007. Supplementary material.

Wood, I.A., Moser, G., Burrell, D.L., Mengersen, K.L. and Hetzel, D.J.S., A meta-analytic assessment of the Thyroglobulin marker for marbling in beef cattle, Genetics Selection Evolution, 38: 479-494, 2006.

Wood, I., Mengersen, K. and Moser, G., Comparative Mapping of Genomes. In G. Smyth et al., editors, Proceedings of the 20th International Workshop on Statistical Modelling. pp. 481-488, 2005.

Gallagher, M., Downs, T. and Wood, I., Empirical Evidence for Ultrametric Structure in Multi-layer Perceptron Error Surfaces,  Neural Processing Letters, 16(2):177-186, 2002.

Wood, I. and Downs, T., Demon Algorithms and their Application to Optimization Problems, In Proc. International Joint Conference on Neural Networks (IJCNN '98), vol. 2, pp. 1661-1666, IEEE Press, New Jersey , 1998.

Wood, I. and Downs, T., Fast Optimization by Demon Algorithms. In Proc. 9th Australian Conference on Neural Networks (ACNN '98), pp. 245-249, UQ, Brisbane, Australia, 1998.

Gallagher, M., Downs, T. and Wood, I.  Ultrametric Structure in Autoencoder Error Surfaces  In L. Niklasson et al., editors, Proc. Eighth International Conference on Artificial Neural Networks (ICANN'98), pp 177-182, Springer, London, 1998.