Madhu Chetty - Research

My research, focused on Bioinformatics and Mental Health, which involves applying Artificial Intelligence (AI), and Machine Learning (ML) to health and life science problems. Being interdisciplinary, my research contributes to two key research centres of FedUni: Health and IT - both being strategically significant.

Key research areas:

  • Modelling Genetic Networks: New AI methods for modelling/optimisation to produce accurate large-scale genetic networks. Under Indo-Australian grant, developed methods for biofuel/carbon sequestration. Models being generic, currently applying for cardio-vascular and eye disease research.
  • Protein Structure Prediction and Drug Design: Novel AI/ML based methods to produce accurate protein structures. In collaboration with IBM, working on causal inference approach for drug repurposing.
  • Mental Health: Novel AI techniques developed for analysing biopsychosocial data for mental health. Similar methods developed for MRFF-NHMRC grant. Investigating ChatGPT and other language models (i.e., BERT) for Current IBM linked project on Crisis Health Support.

Research Grants (as Sole, Lead, Chief or Associate investigators)

  • $299, 976: “Suicide, suicidality, and links to gambling: pathways for prevention”, (2023 -2025)
  • $90, 000: "Big Data and machine learning methods for Sports Analytics", Global Hosts Pty Ltd (Industry Funding), (2018 -2020)
  • $191, 000: “Intent analysis for identification of sports enthusiasts”, GLOBAL HOSTS PTY LTD trading as SPORTSHOSTS, (2021)
  • $ 625, 000: "Solar bio fuel and Carbon Sequestration: Role of genetic network, Australia- India Strategic Research. Cat-1 (2010-2015)
  • $5,000: “Annotating datasets for sentiment analysis to identify sports enthusiasts”, Global Hosts Pty Ltd trading as SportsHosts, Industry funding. (2020)
  • $ 218,139.85: "Implementing artificial intelligence (AI) to enhance Lifeline’s crisis support service capacity in response to COVID-19 and emerging crises”, 2020 COVID-19 Mental Health Research, NHMRC, Cat-1 (2021-2022)
  • $1,278,141: Wellbeing Track & Change: Using digital monitoring to improve the mental wellbeing", Worksafe Victoria: WorkWell Fund, Cat-1, (2019-2021)".
  • $230k: “Coarse grained algorithms for parallel computing for bioinformatics applications, ARC DP grant administered by Griffith University, (2008-2010).
  • $20k stochastic modelling of genetic networks, RUN (Regional University Network) grant (2014).
  • $10k: “A game theoretic approach to agent based modelling for carbon trading”, Monash Uni. - Masdar Institute of Science and Technology,2010
  • $10k, “Multimodal inputs in virtual reality system”, Monash small grant, 2005.
  • $18k: “Modelling and inference of gene regulatory network”, Monash Small grant, 2005
  • $10k: “Can dominance of one sense over another help in virtual reality system design?”, Monash Arts/IT grant, 2003
  • $8.5k: “Application of neural network techniques for control”, Monash small grant, 2003
  • $10k: “Optimal allocation of resources in computational grid”, Monash small grant, 2002

Postgraduate research supervisions

Supervising Post docs/ Research Assistant

  • Dr Abdur Rahman @Fed Uni
  • Dr Zari Dzalilov @FedUni
  • Dr Ahsan Chowdhury @Fed Uni
  • Dr Vinh Nguyen @ Monash University
  • Dr Sandeep Gaudana @IIT Bombay
  • Ms Kriti Nandu @IIT Bombay, India
  • Dr Tamjid Hoque @Griffith University

Current PhD supervision

  • Mr Kewen Ding (IBM-FedUni scholarship) –  Artificial Intelligence and Mental Health
  • Ms Nabila Ramdhanti (IBM-FedUni Scholar): Causal Inference based drug repurposing
  • Ms Deepali Pilodiya (self-funded, domestic): Cancer Classification
  • Mr Ezazul Islam (Henry Sutton scholar): Blockchain based energy trading
  • Ms Jaskaran Kaur (HITC scholar); Natural Language Processing and genetic network modeling
  • Ms Hasini Gamage (Destination Australia scholar): Ensemble of several genetic network models

Completed PhD Supervision

  • Mr Saleem Malik (self-funded) – Blockchain adaption in Australian industries
  • Mr Buddhika Kasturiachachy (Industry funded): Meaning Sensitive Noisy Text Analytics in the Low Data Regime
  • Ms Meena Santhanagopalan (HITC funded): Biopsychosocial Data Analytics and Predictive Modeling
  • Ms Akhila George (under Monash-IITB academy): Enhanced and desirable lipid synthesis in algae for efficient biofuel production
  • Mr. Ahammad Yousef (@Monash): Non-linear Modeling Framework Using Michaelis-Menten Kinetics for Gene Regulatory Networks
  • Mr Ajay Nair (under Mon-IITB academy): a-priori knowledge in reconstructing Bayesian genetic network models
  • Mr. Ahsan Raja Choudhary @Monash : Large scale gene regulatory network reconstruction using S-system modelling paradigm
  • Mr. Nizamul Morshed @Monash: Learning realistic genetic interactions using information theoretic approach
  • Mr. Kamrul Islam @Monash: Memetic approach for prediction of low resolution protein structures using lattice models
  • Mr. Ramesh Ram @Monash: Markov blanket based causal model for reconstruction of gene regulatory network
  • Mr. Md. Tamjidul Hoque@Monash: Genetic algorithm for ab initio protein structure prediction based on low resolution models
  • Ms. Chia Huey Ooi @Monash: Differential prioritisation in feature selection for multi-class molecular classification.
  • Mr. Niranjan Bidargaddi @Monash: Hybrid computational models for protein sequence analysis and secondary structure prediction
  • Mr. A Abraham@Monash: Artificial Neural Network and classification

Masters Completion

  • Sunil Aryal: New generative classifiers with mass-based likelihood estimation
  • Long Tang:  Carbon Trading model

Publications

Edited Books

  1. (Lead Guest Editor) Madhu Chetty, J Hallinan, G Ruz, and A Wipat, Special Issue of Computational Intelligence in Bioinformatics and Computational Biology, Elsevier’s Biosystems journal, 2022 https://www.sciencedirect.com/science/article/pii/S0303264722001733?dgcid=author
  2. Jennifer Hallinan, Madhu Chetty, Gonzalo Ruz Heredia, Adrian Shatte and Suryani Lim, Proc. IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology, https://ieeexplore.ieee.org/xpl/conhome/9562742/proceeding, 2021
  3. Madhu Chetty, Jennifer Hallinan, Suryani Lim and Adrian Shatte and Cameron Foale, 2021 Supplemental Proceedings of the IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology, ISBN (e-version): 978-0-908026-67-8, DOI: https://doi.org/10.25955/1a39-wk54
  4. A. Abraham, D Virmani, O. Castillo, M. Chetty, B Suri, C Gupta, P Girdhar 2020, Special issue in Journal of Information & Optimization Sciences (JIOS): ISSN: 0252-2667; Taylor & Francis.
  5. (Guest Eds) Madalina Drugan, Marco Wiering, Peter Vamplew, Madhu Chetty, (2017) Special Issue on Multiobjective Reinforcement Learning: Theory and Applications, Elsevier Neurocomputing journal, https://doi.org/10.1016/j.neucom.2017.06.020, Vol 263, 2017
  6. (Guest Eds) Chetty M., Ngom A. and Marchiori E., (2010) Computational Intelligence in Bioinformatics, Special issue published by Elsevier Neurocomputing journal (I.F. 3.3).
  7. (Guest Eds) Chetty M., Shandar A., and Bertil S., (2010) Pattern Recognition in Bioinformatics, Special Issue of  Pattern Recognition Letters (I.F.=1.9, journal ERA rank: A).
  8. (Eds) Chetty M., Ngom A. and Shandar A. 2008, Pattern Recognition in Bioinformatics,  Book Series: Lecture Notes in Computer Science, Subseries: Lecture Notes in Bioinformatics, Conference proceedings for Third IAPR International Conference, PRIB 2008, Melbourne, Australia, October 15-17, 2008., Vol. LNBI 5265, 472 pages, ISBN: 978-3-540-88434-7
  9. (Eds) Chetty M., Shandar A., Ngom A., Teng S. W., 2008 Pattern Recognition in Bioinformatics, Supplementary PRIB’08 Conference proceedings, Melbourne, Australia, October 15-17, Publisher: PRIB 2008 ISBN: 978-0-7326-22268.

Book Chapters

  1. Chowdhury A. and Chetty M., 2016, Large-scale reconstruction of gene regulatory network using s-system model, Eds. Hitoshi Iba and Nasimul Noman, Evolutionary Computation in Gene Regulatory Network Research, Wiley Book Series on Bioinformatics, pp185-206,
    https://books.google.co.in/books?hl=en&lr=&id=QlZwCwAAQBAJ&oi=fnd&pg=PA185&dq=info:-JASOa5BVrUJ:scholar.google.com&ots=7rXrvStxVj&sig=Wzmefn7dxQGzX8YAgNfRRFj8S4M#v=onepage&q&f=false  
  2. Ram R. and Chetty M., 2009, “Modeling gene regulatory networks using computational intelligence techniques”, Eds. S. Das et al, Computational Modelling of Gene Regulatory Networks, Publisher: IGI Global USA.
  3. Hoque M. T., Chetty M., Sattar A. 2009, “Genetic Algorithm in Ab Initio Protein Structure Prediction using Low Resolution Model: A Review”, Biomedical data and applications, Eds: Sidhu, A.S., Dillon, T.S. and Chang, E., Springer-Verlag, series in Studies in Computational Intelligence (SCI) series.
  4. Hoque, M.T., Chetty M., Dooley, L.S.  2007, Significance of Hybrid Evolutionary Computation for Ab Inito Protein Folding Prediction, Hybrid Evolutionary Algorithms,Volume 75/2007,ed: Grosan  C., Abraham A. and Ishibuchi H., publisher: Springer-Verlag, Berlin in the Studies in Computational Intelligence (SCI) book series. ISSN: 1860-949X, (Print) 1860-9503 (Online), pp. 241-268.

Referred Journal Articles

  1. S. Yu, F. Xia, Y. Wang, S. Li, F. G. Febrinanto and M. Chetty, "PANDORA: Deep Graph Learning Based COVID-19 Infection Risk Level Forecasting," in IEEE Transactions on Computational Social Systems, doi: 10.1109/TCSS.2022.3229671.
  2. J K Gill, M Chetty, A Shatte and J Hallinan 2022, “Improving S-system GRN models by reducing the number of invalid regulations”, Special Issue in Bioinformatics, Elsevier’s Biosystem Journal, Q1 rank, https://www.sciencedirect.com/science/article/pii/S0303264722001204?via%3Dihub .
  3. H. Gamage, M Chetty, A Shatte and J Hallinan 2022, “Feature selection based boolean modelling for genetic network inference”, Special Issue in Bioinformatics, Elsevier’s Biosystem Journal, Q1 rank, https://www.sciencedirect.com/science/article/pii/S0303264722001381?via%3Dihub .
  4. Buddhika Kasturiachi, Madhu Chetty, Adrian Shatte, Darren Walls, 2021, From General Language Understanding to Noisy Text Comprehension, Applied Sciences journal, Special Issue on Natural Language Processing, Vol 11, No 17, ISSN 2076-3417, No 7814
  5. Rumana Nazmul, Madhu Chetty and Ahsan Chowdhury, 2021, An Improved Memetic Approach for Protein Structure Prediction Incorporating Maximal Hydrophobic Core Estimation Concept, Elsevier's Knowledge Based Systems, Vol 219, 104395, https://doi.org/10.1016/j.knosys.2018.06.022, Impact factor 8.038
  6. MD Malik, Mehmood Chadhar, Savanid Vatanasakdakul, Madhu Chetty, 2021, Factors Affecting the Organizational Adoption of Blockchain Technology: Extending the Technology–Organization–Environment (TOE) Framework in the Australian Context, MDPI Sustainability 2021, 13, 9404, https://doi.org/10.3390/su13169404 Impact Factor 3.251
  7. Rumana Nazmul, Madhu Chetty and Ahsan Chowdhury, 2020 Multi-modal Memetic Framework for Low Resolution Protein Structure Prediction, Elsevier’s Swarm and Evolutionary Computation journal, Vol 52, Feb 2020, 100608,  https://doi.org/10.1016/j.swevo.2019.100608, Impact factor 7.177
  8. Ahammed SK Yousef, Madhu Chetty and Gour Karmakar, 2019, "Reverse engineering genetic networks using nonlinear saturation kinetics", Biosystems, Elsevier, Volume 182, Pages 30-41, https://doi.org/10.1016/j.biosystems.2019.103977, Impact factor 1.973
  9. Meena Santhanagopalan, Madhu Chetty, Cameron Foale, Britt Klein, 2019,  Towards Machine Learning approach for Digital-Health intervention program, Aust. Journal of Intelligent Information Process. Syst. 15(3): pages 16-24 (2019) http://ajiips.com.au/papers/V15.4/v15n4\_20-28.pdf
  10. Ahammed SK Yousef, Madhu Chetty and Gour Karmakar, 2018, PCA based population generation for genetic network optimization, Cognitive Neurodynamics, Springer Publisher, https://link.springer.com/article/10.1007%2Fs11571-018-9486-0, pp. 1-13.
  11. S Krishnakumar, Sandeep B Gaudana, Xuan Vinh Nguyen, Ganesh A Viswanathan, Madhu Chetty, Pramod P Wangikar, 2015, “Coupling of cellular processes and their coordinated oscillations under continuous light in cyanothece sp. ATCC 51142, a diazotrophic unicellular cyanobacteria”, PLOS ONE, Impact factor 3.534
  12. Ahsan Chowdhury and Madhu Chetty, 2015, Network Decomposition for Large-scale Reverse Engineering of Gene Regulatory Network, Elservier Neurocomputing, pp. 213-227, http://dx.doi.org/10.1016/j.neucom.2015.02.020,  Impact Factor 2.12
  13. Influence of mixotrophic growth on rhythmic oscillations in expression of metabolic pathways in diazotrophic cyanobacterium Cyanothece sp. ATCC 51142, Pramod Wangikar, S Krishnakumar,Sandeep B Gaudana, Madhuri G Digmurti, Ganesh A Viswanathan, Madhu  Chetty,  Bioresource Technology, Impact factor 4.494
  14. Ahsan Raja Chowdhury, Madhu Chetty, and Rob Evans, “Stochastic S-system Modeling of Gene Regulatory Network”, Springer Journal of Cognitive Neurodynamics, DOI 10.1007/s11571-015-9346-0. Impact factor 1.671
  15. Ajay Nair, Madhu Chetty and Pramod Wangikar, “Improving gene regulatory network inference using network topology information, Molecular BioSystems, Impact factor 3.183
  16. Ahsan Chowdhury, Madhu Chetty, Vinh Nguyen, 2014, Evaluating Influence of microRNA in Reconstructing Gene Regulatory Networks,Cognitive Neurodynamics, Springer Publication, Vol. 8, Issue 3, pp. 251-259, ISSN: 1871-4099,  Impact Factor 1.742.
  17. Sandeep Bhupendra Gaudana, S Krishnakumar, Swathi Alagesan, Madhuri Gopal Digmurti, Ganesh A Viswanathan, Madhu Chetty, Pramod P. Wangikar, 2013, Rhythmic and sustained oscillations in metabolism and gene expression of Cyanothece sp. ATCC 51142 under constant light, Front. Microbiol. 4:374. doi: 10.3389/fmicb.2013.00374. Impact Factor 3.9
  18. Sandeep B Gaudana, Swathi Alagesan, Madhu Chetty and Pramod P Wangikar, 2013, Diurnal rhythm of an unicellular diazotrophic cyanobacterium under mixotrophic conditions and elevated carbon dioxide, Photosynthesis Research, Vol. 118, Issue 1-2, pp 51-57, PubMed, ISSN: 0166-8595, , Impact Factor 3.15
  19. Ahsan Chowdhury, Madhu Chetty and Vinh Nguyen, 2013, Incorporating time-delays in S-System model for reverse engineering genetic networks, BMC Bioinformatics 2013, 14:196, ISSN: 1471-2105, , Impact Factor: 3.024
  20. Vinh Nguyen, Madhu Chetty, Ross Coppel, Sandeep Gaudana, Pramod Wangikar, 2013, A Model of the Circadian Clock in the Cyanobacterium Cyanothece sp. ATCC 51142, BMC Bioinformatics,Vol. 14 (Suppl 2),  ISSN: 1471-2105, , Impact Factor: 3.024
  21. Islam K and Chetty M., 2013, Clustered Memetic Algorithm with Local Heuristics for ab initio Protein Structure Prediction, IEEE Transactions on Evolutionary Computation, ISSN : 1089-778X, Vol 17, issue 4, pp. 558-576,   Impact Factor: 8.5
  22. Vinh N.X, Chetty M., Coppel R. and  Wangikar P. P, 2012, Gene Regulatory Network Modeling via Global Optimization of High-Order Dynamic Bayesian Network, BMC Bioinformatics,13:131, ISSN: 1471-2105, Highly accessed category, Impact Factor: 3.024
  23. Nizamul Morshed, Madhu Chetty and Vinh Nguyen, 2012, Simultaneous learning of instantaneous & time-delayed genetic interactions using novel information theoretic scoring technique, BMC Systems Biology[P], 6:62,  vol 6, issue 62, BioMed Central, ISSN:1752-0509, pp. 1-15,  Impact Factor: 2.982
  24. Vinh Nguyen, Madhu Chetty, Pramod Wangikar and Ross Coppel, 2012, Issues impacting  Validating Genetic Network Reverse Engineering Algorithms Using Small Networks,  Elsevier’s Biochimica et Biophysica Acta -  Proteins and Proteomics,  ISSN: 1570-9639.  vol 1824, issue 12, Elsevier BV, The Netherlands, pp. 1434-1441, Impact Factor: 3.773.
  25. Vinh, N. X., Chetty, M., Coppel, R., and Wangikar, 2011, GlobalMIT: Learning globally optimal dynamic Bayesian network with the mutual information test criterion, Bioinformatics, ISSN:1367-4803, Volume 27, Issue 19, pages 2765-2766,  *, Impact Factor: 5.323
  26. Hoque M.T. ,  Chetty M. , Lewis A. and Sattar A., 2011, Twin-Removal in Genetic Algorithms for Protein Structure Prediction using Low Resolution Model, IEEE/ACM Transactions on Computational Biology and Bioinformatics, ISSN: 1545-5963, vol. 8, No. 1, pp.234-245. Impact Factor: 1.616
  27. Ram R and Chetty M., 2011, A Markov Blanket based Model for Gene Regulatory Network Inference, IEEE/ACM Trans. in computational biology and bioinformatics, ISSN: 1545-5963, Vol. 8, No. 2, pp. 353-367. Impact Factor: 1.616
  28. Hoque M.T. , Chetty M., Lewis A., Sattar A., and Avery V., 2010, DFS Generated Pathways in GA Crossover for Protein Structure Prediction", Elsevier's Neurocomputing journal, ISSN: 0925-2312, Special issue on Computational Intelligence in Bioinformatics, Volume 73, pages 2308-2316. Impact Factor: 1.634
  29. Hoque M. T., Chetty M., Sattar A., 2009, Extended HP Model for Protein Structure Prediction, Journal of Computational Biology, ISSN: 1066-5277, Volume 16, No. 1,  pp. 85–103, Impact Factor: 1.564
  30. Bidargaddi N.P, Chetty M and Kamruzzaman, 2009, Combining Segmental Semi-Markov Models with Neural Networks for Protein Secondary Structure Prediction Elsevier’s Neucomputing Journal, ISSN: 0925-2312, vol 72, issue 16-18, pp. 3943-395, Impact Factor: 1.634.
  31. Ooi C.H., Chetty M., Teng S.W., 2009, Predicting the optimal degree of differential prioritization for multiclass molecular classification, IEEE Engineering in medicine and Biology, ISSN: 0739-5175 Impact Factor 2.727
  32. Bidargaddi N.P., Chetty M., Kamruzzaman J. 2008, Hidden Markov models incorporating fuzzy measures and integrals for protein sequence identification and alignment, Elsevier’s Journal of Genomics, Proteomics and Bioinformatics, vol. 6, No.2, ISSN 1672-0229, pp 98-110.
  33. Ooi, C. H, Chetty, M., Teng S. W 2007, Characteristics of predictor sets found using differential prioritization, Algorithms for Molecular Biology, 2007, 2:7, ISSN:1748-7188,  Impact Factor: 1.606.
  34. Ooi, C. H, Chetty, M., Teng S. W, 2006, Differential Prioritization between Relevance and Redundancy in Correlation-based Feature Selection Techniques for Multiclass Gene Expression Data, BMC Bioinformatics Vol. 7, No. 320, ISSN: 1471-2105. , Impact Factor: 3.024
  35. Ooi, C. H, Chetty, M., Teng S. W 2006, Differential Prioritization in Feature Selection and Classifier Aggregation for Multiclass Microarray Datasets, Data Mining and Knowledge Discovery, Vol 14, Issue 3, pp. 329-366,  ISSN: 1384-5810, Impact Factor: 2.877, *
  36. Bidargaddi, N., Chetty, M., Kamruzzaman, J., 2006, Fuzzy Measures and Integrals in Profile Hidden Markov Models for Protein Sequence Analysis, Journal of intelligent and fuzzy systems, Applications in Engineering and Technology ISSN: 1064-1246, vol. 17, No. 6 pp.541-556. Impact Factor: 0.788
  37. Hoque, M. T, Chetty M., Dooley, L. S, 2005, Fast Computation of the Fitness Function for Protein Folding Prediction in a 2D Hydrophilic-Hydrophobic Model, International Journal of Simulation: Systems, Science & Technology, vol 6, United Kingdom Simulation Society, UK, pp. 27-37.
  38. Chetty, M. , Hu, S. T, Bennett, J. A, 2003, An interactive Java-based educational module in electromagnetics, International Journal of Electrical Engineering Education, vol 40, ed., Manchester University Press, Manchester UK, pp. 79-90
  39. Chetty, M., 2002, A Fuzzy Logic Based Discrete Mode Power System Stabilizer, Asian Journal of Control, ISSN: 1561-8625, vol 4, ed ,  pp. 327-332. Impact Factor 1.411.
  40. Chetty M., Buyya, R., 2002, Weaving Computational Grids: How Analogous Are They with Electrical Grids?, IEEE Computing in Science & Engineering, ISSN: 1521-9615, vol 4, No. 4, IEEE Computer Society, USA, pp. 61-71 Impact Factor 1.729

Peer Referred Conference Articles

  1. J K Gill, M Chetty, A Shatte and J Hallinan, 2022 “Improving S-system GRN models by reducing the number of invalid regulations”, IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2022.
  2. H. Gamage, M Chetty, A Shatte and J Hallinan, 2022 “Feature selection based boolean modelling for genetic network inference”, IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2022.
  3. Md Ezazul Islam, Madhu Chetty, S Lim, M Chadhar, and S Islam, 2022 Incorporating Price Information in Blockchain-based Energy Trading, Twenty-eighth Americas Conference on Information Systems (AMCIS), CORE-A rank.
  4. Hasini Gamage, Madhu Chetty, Adrian Shatte, Jennifer Hallinan, 2021, A Boolean Network Model based approach for efficient Gene Regulatory Network inference, IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2021, 2nd student paper prize from IEEE Victorian Section.
  5. Jaskaran Gill, Madhu Chetty, Adrian Shatte, Jennifer Hallinan, 2021, Dynamically Regulated Initialization for S-system Modelling of Genetic Networks, IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2021, 3rd student paper prize from IEEE Victorian Section.
  6. Buddhika Kasturiachi, Madhu Chetty, Adrian Shatte, Darren Walls, 2021 “Cost Effective Annotation Framework Using Zero-shot Text Classification”, IEEE IJCNN, World Congress on Computational Intelligence (WCCI), July 2021
  7. MS Malik, M Chadhar, M Chetty, 2021, Factors Affecting the Organizational Adoption of Blockchain Technology: An Australian Perspective, Proceedings of the 54th Hawaii International Conference on System Sciences, 5597
  8. Buddhika Kasturiachi, Madhu Chetty, Gour Karmakar and Darren Walls, 2020, “Pre-trained Language Models with Limited Data for Intent Classification, IEEE IJCNN, World Congress on Computational Intelligence (WCCI), July 2020.
  9. Malik, S., Chadhar, M., Chetty, M., & Vatanasakdakul, S. (2020). An Exploratory Study of the Adoption of Blockchain Technology Among Australian Organizations: A Theoretical Model. In European, Mediterranean, and Middle Eastern Conference on Information Systems, pp. 205-220, Springer, Cham.
  10. Malik, S., Chadhar, M. A., Chetty, M., & Vatanasakdakul, S. (2020). Adoption of Blockchain Technology among Australian Organizations: A Mixed-Methods Approach, ACIS 2020 Proceedings, https://aisel.aisnet.org/acis2020/16.
  11. Mohammad Mahabub Alam, Gour Karmakar, Syed Islam, Joarder Kamruzzaman, Madhu Chetty, Suryani Lim, Gayan Appuhamillage, Gopi Chattopadhyay, Steve Wilcox and Vincent Verheyen,, Assessing Transformer Oil Quality using Deep Convolutional Networks, AUPEC 2019.
  12. Shipra Chinna, Mehmood Chadhar, Madhu Chetty and Nui Savanid, 2019, Challenges and Opportunities for blockchain technology adoption: A systematic review, Australian Conference on Information systems.
  13. Saleem Malik, Madhu Chetty and Mehmood Chadhar, 2018, “A Information Technology and Organizational Learning Interplay: A Survey”, Australasian Conference on information Systems, December 2018.
  14. Meena Santhanagopalan, Madhu Chetty, Cameron Foale, Sunil Aryal, Britt Klein 2018, Relevance of frequency of heart-rate peaks as indicator of `biological' stress level, Proc. 25th International Conference on Neural Information Processing (ICONIP), Cambodia December 2018.
  15. Meena Santhanagopalan, Madhu Chetty, Cameron Foale, Sunil Aryal, Britt Klein 2018, Modeling neurocognitive reaction time with Gamma distribution 11th Australasian Conference on Health Informatics and Knowledge Management, Australian Conference on health informatics and knowledge management.
  16. A.S.K. Youseph, Madhu Chetty and Gour Karmakar, 2018 'Large Scale Modeling of Genetic Networks Using Gene Knockout Data', 11th Australasian Conference on Health Informatics and Knowledge Management, Australian Conference on health informatics and knowledge management.
  17. Ajay Nair and Madhu Chetty, 2017, Post-inference Methods Of Prior Knowledge Incorporation In Gene Regulatory Network Inference, bioRxiv, https://doi.org/10.1101/122341.
  18. A Nair, M Chetty, NX Vinh, 2017, ‘RegCyanoDB: a database of cyanobacterial regulatory interactions, ’bioRxiv, doi: https://doi.org/10.1101/117127
  19. Ahammed Sherief Kizhakkethil Youseph, Madhu Chetty and Gour Karmakar, 2016, Enhancing Gene Regulatory Network Inference Exploiting Temporal Correlations of Expression Profile, Proc. 21st International Conference on Neural Information Processing (ICONIP),
  20. Ahammed Sherief Kizhakkethil Youseph, Madhu Chetty and Gour Karmakar, 2015, A New Model for Gene Regulatory Network Inference using Michaelis-Menten Kinetics, IEEE Conference on Evolutionary Computation, 2015.
  21. Rubaiya Khan and Madhu Chetty, 2015, “Towards Large Scale Genetic Network Modelling, IEEE CIBCB.
  22. Ahammed Sherief Kizhakkethil Youseph, Madhu Chetty and Gour Karmakar, 2015, “Decoupled Modeling of Gene Regulatory Networks using Michaelis-Menten Kinetics”, IEEE ICONIP 2015,.
  23. Abdur Rahman, Madhu Chetty, Dieter Bulach and Pramod Wangikar, “Frequency Decomposition based Gene Clustering, ICONIP 2015
  24. Rumana Nazmul and Madhu Chetty, 2014, Sib-based Survival Selection Technique for Protein Structure Prediction, Proc. 21st International Conference on Neural Information Processing (ICONIP), LNCS Volume 8835,  pp. pp 470-478, .
  25. Ajay Nair, Madhu Chetty and Pramod Wangikar, Signicance of Non-Edge Priors in Gene Regulatory Network Reconstruction Proc. 21st International Conference on Neural Information Processing (ICONIP), LNCS Volume 8834, pp 446-453,
  26. Rumana Nazmul and Madhu Chetty, 2013, A Knowledge-based Initial Population Generation in Memetic Algorithm for Protein Structure Prediction, In Proc. 20th International Conference on Neural Information Processing (ICONIP),  pp. 546-553
  27. Rumana Nazmul and Madhu Chetty, 2013, Protein Structure Prediction with a New Composite Measure of Diversity and Memory-based Diversification Strategy, In Proc. 20th International Conference on Neural Information Processing (ICONIP),  pp. 649-656,
  28. Ahsan Raja Chowdhury, Madhu Chetty, and Nguyen Xuan Vinh, 2013, On the Analysis of Time-delayed Interactions in Genetic Network using S-System Model, In Proc. 20th International Conference on Neural Information Processing (ICONIP), pp. 616–623,
  29. Ahsan Raja Chowdhury, Madhu Chetty, and Nguyen Xuan Vinh, 2013, Reverse Engineering Genetic Networks with Time-delayed S-System Model and Pearson Correlation Coefficient, In Proc. 20th International Conf. on Neural Information Processing (ICONIP), 624–631,
  30. Rumana Nazmul and Madhu Chetty, 2013, "An Adaptive Strategy for Assortative Mating in Genetic Algorithm", IEEE Congress on Evolutionary Computation,  pp. 125-126,
  31. Ahsan Raja Chowdhury, Madhu Chetty and Vinh Nguyen, 2013, “Inferring large scale genetic networks with S- System model”,  Genetic and Evolutionary Computation Conference. pp. 271-278, .
  32. Nizamul Morshed, Madhu Chetty, Vinh Nguyen, Terry Caelli, 2013, “mDBN: Motif-Based Learning of Dynamic Bayesian Network for the Reconstruction of Gene Regulatory Networks” , Genetic and Evolutionary Computation Conference, pp. 279-286, .
  33. Rumana Nazmul and Madhu Chetty, 2013, “A priority based parental selection method for genetic algorithm, Genetic and Evolutionary Computation Conference, pp. 2237 - 2244. .
  34. Vinh Nguyen, Madhu Chetty, Ross Coppel, Sandeep Gaudana, Pramod Wangikar, 2013, A Model of the Circadian Clock in the Cyanobacterium Cyanothece sp. ATCC 51142, BMC Bioinformatics, Volume 14, Supplement 2, 2013: Selected articles from the Eleventh Asia Pacific Bioinformatics Conference (APBC 2013) Bioinformatics,  doi:10.1186/1471-2105-14-S2-S14 ,  (Awarded Best Paper prize)
  35. Vinh Nguyen, Madhu Chetty, Ross Coppel and Pramod Wangikar, 2012,  Local and Global Algorithms for Learning Dynamic Bayesian Networks, IEEE conference on data mining (ICDM), pp. 685-694 (Acceptance rate 10.7%)
  36. Ahsan Raja Chowdhury, Madhu Chetty and Vinh Nguyen, 2012, Reconstructing genetic network from partial microarray data, In Proc. International Conference on Neural Information Processing (ICONIP), NEURAL INFORMATION PROCESSING, Lecture Notes in Computer Science, Vol 7663, pp 689-696, .
  37. Nizamul Morshed, Madhu Chetty and Vinh Nguyen, 2012, FusGP: Bayesian Co-Learning of Gene Regulatory Networks and Protein Interaction Networks, In Proc. International Conference on Neural Information Processing (ICONIP), NEURAL INFORMATION PROCESSING, Lecture Notes in Computer Science, Vol. 7667,  pp 369-377,  .
  38. Vinh Nguyen, Madhu Chetty, Ross Coppel, Pramod Wangikar, 2012, Data Discretization for Dynamic Bayesian Network based Modeling of Genetic Networks,  In Proc. International Conference on Neural Information Processing (ICONIP), NEURAL INFORMATION PROCESSING, Lecture Notes in Computer Science, ISBN 978-3-642-34480-0, vol. 7664,  pp 298-306, .
  39. Ahsan Raja Chowdhury, Madhu Chetty and Vinh Nguyen, 2012, Adaptive Regulatory Genes Cardinality for Reconstructing Genetic Networks, World Congress on Computational Intelligence (WCCI) 2012, pp. 955-962, .
  40. Rumana Nazmul, Madhu Chetty, Ram Samudrala and David Chalmers, 2012, Protein Structure Prediction based on Optimal Hydrophobic Core Formation, World Congress on Computational Intelligence ( WCCI) 2012, pp. 1263-1271,
  41. Vinh Nguyen, Madhu Chetty, Ross Coppel and Pramod Wangikar, 2012, On Validating Genetic Network Reverse Engineering Algorithms Using Small Networks, tenth Asia Pacific Bioinformatics Conference (APBC), Melbourne, pp.533-554.
  42. Morshed N. and Chetty M., 2011, Reconstructing Genetic Networks with Concurrent Representation of Instantaneous and Time-Delayed Interactions, IEEE Conf. Evolutionary Computation. 1840-1847 .
  43. Islam K, Chetty M. and Morshed M., 2011, Novel Local Improvement Techniques in Clustered Memetic Algorithm for Protein Structure Prediction, IEEE Conf. Evolutionary Computation, pages 1003 – 1011,
  44. Choudhry A and Chetty M., 2011, An Improved Method to Infer Gene Regulatory Network using S-System, IEEE Conf. Evolutionary Computation, pp.1012-1019, New Orleans, USA. .
  45. Morshed N. and Chetty M. 2011, Information theoretic dynamic Bayesian network approach for reconstructing genetic networks, The Eleventh IASTED International Conference on Artificial Intelligence and Applications (AIA'2011), pp. 236-243, Innsbruk, Austria.
  46. Nizamul Morshed, Madhu Chetty, Nguyen Xuan Vinh, 2011, Simultaneous Learning of Instantaneous and Time-Delayed Genetic Interactions Using Novel Information Theoretic Scoring Technique. ICONIP (2), pp: 248-257.
  47. Nizamul Morshed, Madhu Chetty, 2011,  Combining Instantaneous and Time-Delayed Interactions between Genes - A Two Phase Algorithm Based on Information Theory. Australasian Conference on Artificial Intelligence,pp  102-111, Australia.
  48. M K Islam, M Chetty, and M Murshed, 2011, Conflict resolution based global search operators for long protein structure prediction. In Proc. International Conference on Neural Information Processing (ICONIP), NEURAL INFORMATION PROCESSING, Lecture Notes in Computer Science, 2011, Volume 7062/2011, 636-645, DOI: 10.1007/978-3-642-24955-6_75
  49. M K Islam, M Chetty, A D Ullah, and K Steinhöfel 2011, A memetic approach to protein structure prediction in triangular lattices. In Proc. International Conference on Neural Information Processing (ICONIP), 2011. NEURAL INFORMATION PROCESSING, Lecture Notes in Computer Science, Volume 7062/2011, 625-635, DOI: 10.1007/978-3-642-24955-6_74
  50. Vinh Nguyen, Madhu Chetty, Pramod Wangikar and Ross Coppel 2011, Dynamic Bayesian Network Modeling of Cyanobacterial Biological Processes via Gene Clustering ICONIP. , Lecture Notes in Computer Science, 2011, Volume 7062/2011
  51. Vinh Nguyen, Madhu Chetty, Pramod Wangikar and Ross Coppel 2011, Polynomial Time Algorithm for Learning Globally Optimal Dynamic Bayesian Network ICONIP. , Lecture Notes in Computer Science, 2011, Volume 7062/2011
  52. Long Tang, Madhu Chetty and Suryani Lim 2011, Multi agent carbon trading incorporating human traits and game theory, ICONIP 2011. , Lecture Notes in Computer Science, 2011, Volume 7062/2011
  53. Islam K. and Chetty M. 2010, Clustered Memetic  Algorithm for Protein  Structure Prediction, IEEE Conference on evolutionary computation (WCCI 2010), July2010, Barcelona, Spain.
  54. Chanthaphavong S and Chetty M., 2010, Binary-Organoid Particle Swarm Optimisation for Inferring Genetic Regulatory Networks, IEEE Conference on evolutionary computation (WCCI 2010), July2010, Barcelona.
  55. Chetty G. and Chetty M., 2010, Multiclass Microarray Gene Expression Classification Based on Fusion of Correlation Features, Fusion 2010, Proceedings IEEE International Conference on Information Fusion, July 2010, Edinburgh, UK.
  56. Ram R. and Chetty M. 2009, MCMC Based Bayesian Inference for Modeling Gene Networks, Lecture Notes in Bioinformatics, Springer Verlag, Berlin Germany, LNBI 5780 , Pattern Recognition in Bioinformatics (PRIB 2009), Sheffield, pp.293-306.
  57. Islam K. and Chetty M. 2009, Novel memetic algorithm for protein structure prediction, Lecture Notes in Artificial Intelligence, Springer Verlag, Berlin Germany, LNAI 5866, Melbourne.
  58. Chetty G and Chetty M. 2009, Multiclass Microarray gene expression analysis based on mutual dependency models, Lecture Notes in Bioinformatics, Springer Verlag, Berlin Germany, LNBI5780 , Pattern Recognition in Bioinformatics (PRIB 2009), Sheffield, pp. 46-55.
  59. Ram R and Chetty M. 2008, Generating Synthetic Gene Regulatory Networks, Pattern recognition in bioinformatics, Lecture Notes in Bioinformatics, Springer Verlag, Berlin Germany, LNBI 5265, Pattern Recognition in Bioinformatics (PRIB 2008), Melbourne, pp. 237-249.
  60. Ram R and Chetty M., D Bulach, 2008, Constraint Logic Minimization for Efficient Modeling of Gene Regulatory Network, Lecture Notes in Bioinformatics,  Springer Verlag, Berlin Germany, LNBI 5265,  Pattern Recognition in Bioinformatics (PRIB 2008), Melbourne, pp. 201-212.
  61. Hoque M. T., Chetty M., Lewis A. and Sattar A. 2008, DFS based Partial Pathways in GA for Protein Structure Prediction, Lecture Notes in Bioinformatics, Springer Verlag, Berlin Germany,  Vol. LNBI 5265, Pattern Recognition in Bioinformatics, (PRIB 2008), Melbourne, pp.41-53.
  62. Ooi C. H., Teng S. and Chetty M. 2008, A study on the importance of differential prioritization in feature selection using toy datasets, Lecture Notes in Bioinformatics, Pattern Recognition in Bioinformatics (PRIB 2008), Melbourne, pp. 311-322.
  63. Ram R. and Chetty M. 2007, A Framework for Path Analysis in Gene Regulatory Networks, Lecture Notes in Bioinformatics, Pattern Recognition in Bioinformatics (PRIB 2007), Springer Verlag, Berlin Germany, October 2007, Singapore, pp. 264-273.
  64. Hoque M. T., Chetty M. and Dooley L.S. 2007, Generalized Schemata Theorem Incorporating Twin Removal for Protein Structure Prediction, Lecture Notes in Bioinformatics, Springer Verlag, Berlin Germany, Pattern Recognition in Bioinformatics (PRIB 2007), October 2007, Singapore, pp. 84-93.
  65. Hoque M. T., Chetty M. and Sattar A. 2007, Protein Folding Prediction in 3D FCC HP Lattice Model Using Genetic Algorithm, IEEE Conference on evolutionary computation, September 2007, Singapore, pp. 4138-4145 .
  66. Ram R and Chetty M. 2007, A guided genetic algorithm for learning gene regulatory network, IEEE congress on evolutionary computation, September 2007, Singapore, pp. 3862- 3869  .
  67. Ram R and Chetty M. 2007, “Learning Structure of Gene Regulatory Networks”, 6th IEEE International Conference on Computer and Information Science (ICIS 2007), July 2007, Melbourne, Australia, pp. 525-531.
  68. Gondal I., Ashwathnarayaniah S., Chetty M., 2007, Prescription Approach for Uniform Assessment Development Tasks for Distributed Teaching in Monash, 17th Annual Conference of the Australasian Association for Engineering Education, New Zealand.
  69. Ooi, C. H, Chetty, M. , Teng, S. W, 2006, Investigating the Class-Specific Relevance of Predictor Sets Obtained from DDP-based Feature Selection Technique,  In: Rajapakse, J.C., and Wong, L. (Eds.), Workshop on Pattern Recognition in Bioinformatics (PRIB`06), LNBI, 2006, Hong Kong,   ©Springer-Verlag, pp.60-70 .
  70. Ooi, C. H, Chetty M. , Teng, S. W, 2006, OVA Scheme vs. Single Machine Approach in Feature Selection for Microarray Datasets, In: Perner, P. (Ed.), Advances in data mining, The 6th Industrial Conference on Data Mining (ICDM 2006), LNAI 4065, pp. 10–23, 2006, Germany, © Springer-Verlag .
  71. Hoque M. T., Chetty M. and Dooley L.S., 2006, A Hybrid Genetic Algorithm for 2D FCC Hydrophobic-Hydrophilic Lattice Model to Predict Protein Folding,  Advances in Artificial Intelligence, Lecture notes in computer science, 0302-9743 (Print) 1611-3349 (Online), Vol. 4304/2006, DOI 10.1007/11941439, pp. 867-876.
  72. Bidargaddi N.P., Chetty M. ,  and Kamruzzaman J. 2006, Bayesian Segmentation using Residue Proximity for Secondary Structure and Contact Prediction, IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, (CIBCB), 28-29 Sept , Toronto, Canada, pp. 79-86.
  73. Ram R. , Chetty M., and Dix T. 2006, Causal Modeling of Gene Regulatory Network, 2006 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, (CIBCB), 28-29 Sept , Toronto, Canada, pp. 132-139.
  74. Hoque M. D., Chetty M. , and Dooley L.S., 2006,  Non-Isomorphic Coding in Lattice Model and its Impact for Protein Folding Prediction Using Genetic Algorithm, IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, (CIBCB), Vancouver, 28-29 Sept , Toronto, Canada, 71-78.
  75. Ram R., Chetty M., and Dix T.I., 2006, Fuzzy Model for Gene Regulatory Network, IEEE Congress on Computational Intelligence, (WCCI 06), Vancouver Canada, pp. 5599-5604.
  76. Hoque, M. T, Chetty, M., Dooley, L. S, 2006, A Guided Genetic Algorithm for Protein Folding Prediction Using 3D Hydrophobic-Hydrophilic Model, IEEE Congress on Computational Intelligence (WCCI 06), Special session of CEC2006 on Bioinformatics Vancouver Canada, pp.8103-8110.
  77. Bidargaddi, N., Chetty M., Kamruzzaman, J., 2005, Evaluation of Fuzzy Measures in Profile Hidden Markov Models for Protein Sequences, Lecture Notes in Bioinformatics, vol 3745, ed , Springer-Verlag, Berlin Germany, pp. 355-366.
  78. Bidargaddi, N., Chetty M. , Kamruzzaman, J., 2005, A Fuzzy Viterbi Algorithm for Improved Sequence Alignment and Searching of Proteins, Lecture Notes in Computer Science, vol 3449, ed , Springer-Verlag, Berlin Germany, pp. 11-21.
  79. Hoque, M. T, Chetty M. , Dooley, L. S, 2005, Efficient Computation of Fitness Function by Pruning in Hydrophobic-Hydrophilic Model, Lecture Notes in Bioinformatics, vol 3745, ed , Springer-Verlag, Berlin Germany, pp. 346-354
  80. Ooi, C. H, Chetty M. , 2005, A Comparative Study of Two Novel Predictor Set Scoring Methods, Lecture Notes in Artificial Intelligence, vol 3578, ed , Springer-Verlag, Berlin Germany, pp. 432-439.
  81. Ooi, C. H, Chetty M. , 2005, Increasing Classification Accuracy by Combining Adaptive Sampling and Convex Pseudo-Data, Lecture Notes in Artificial Intelligence, vol 3518, ed , (PAKDD) Springer-Verlag, Berlin Germany, pp. 578-587.
  82. Ooi, C. H, Chetty M. , Teng, S. W, 2005, Relevance, Redundancy and Differential Prioritization in Feature Selection for Multiclass Gene Expression Data, Biological and Medical Data Analysis, Lecture Notes in Bioinformatics, vol 3745, Springer-Verlag, Berlin Germany, pp. 367-378
  83. Bidargaddi, N., Chetty, M. , Kamruzzaman, J., 2005, An Architecture Combining Bayesian segmentation and Neural Network Ensembles for Protein Secondary Structure Prediction, 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2005, IEEE Service Center, Piscataway NJ USA, pp. 498-505.
  84. Bidargaddi, N., Chetty, M., Kamruzzaman, J., 2005, Fuzzy Decoding in Profile Hidden Markov Models For Protein Family Identification, International Conference Advances in Bioinformatics and Its Applications, 16/12/2004 to 19/12/2004, ICBA 2004, World Scientific Publishing Co. Pte. Ltd., Singapore, pp. 119-131
  85. Bidargaddi, N., Chetty, M., Kamruzzaman, J., 2005, An Incremental Constructive Layer Neural network Based Power System Stabiliser, 24th IASTED International Conference on Modelling, Identification and Control, 16/02/05 to 18/02/05, MIC 2005, ACTA Press, Anaheim CA USA, pp. 316-321
  86. Bidargaddi, N., Chetty, M., Kamruzzaman, J., 2005, Fuzzy Profile Hidden Markov Models for Protein Sequence Analysis, 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, 14/11/05 to 15/11/05, CIBCB 2005, IEEE Service Center, Piscataway, pp. 427-434
  87. Chetty, M. , 2005, A Frequency Response Based Design of Suboptimal Reduced Order Controller, 24th IASTED International Conference on Modelling, Identification and Control, 16/02/05 to 18/02/05, MIC 2005, ACTA Press, Anaheim CA USA, pp. 52-57
  88. Hoque, M. T, Chetty, M., Dooley, L. S, 2005, A New Guided Genetic Algorithm for 2D Hydrophobic-Hydrophilic Model to Predict Protein Folding, The 2005 IEEE Congress on Evolutionary Computation, 02/09/05 to 05/09/05, IEEE CEC 2005, IEEE Service Center, Piscataway NJ, pp. 259-266.
  89. Hoque, M. T, Chetty, M., Dooley, L. S, 2005, Partially Computed Fitness Function Based Genetic Algorithm for Hydrophobic-Hydrophilic Model, Fourth International Conference on Hybrid Intelligent Systems, 05/12/04 to 08/12/04, Fourth International Conference on Hybrid Intelligent Systems, IEEE Computer Society, Los Alamitos CA, pp. 291-296.
  90. Hoque, M. T, Chetty, M., Dooley, L. S, 2005, An Efficient Algorithm for Computing the Fitness Function of a Hydrophobic-Hydrophilic Model, Fourth International Conference on Hybrid Intelligent Systems, 05/12/04 to 08/12/04, Fourth International Conference on Hybrid Intelligent Systems, IEEE Computer Society, Los Alamitos CA, pp. 285-290.
  91. Ooi, C. H, Chetty, M., Gondal, I., 2005, The Role of Feature Redundancy in Tumor Classification, Proceedings of the International Conference Advances in Bioinformatics and its Applications, 16/12/04 to 19/12/04, International Conference Advances in Bioinformatics and its Applications, World Scientific Publishing Co. Pte. Ltd., Singapore, pp. 197-208
  92. Ooi, C. H, Chetty, M., Teng, S. W, 2005, Modeling Microarray Datasets for Efficient Feature Selection, Proceedings 4th Australasian Data Mining Conference, 05/12/06 to 06/12/05, Australasian Data Mining Conference 2005, University of Technology, Sydney NSW Australia, pp. 115-131.
  93. Biddargaddi, N. P., Chetty, M., 2003, An Incremental Constructive Layer Algorithm for Controller Design, Frontiers in Artificial Intelligence and Applications: Design and Application of Hybrid Intelligent Systems, vol 104, IOS Press, THE NETHERLANDS, pp.58-65.
  94. Biddargaddi, N., Chetty, M., 2003, Investigating Circular Kohonen Layer for Control Application, The Second International Conference on Computational Intelligence, Robotics and Autonomous Systems, 15/12/03 - 18/12/03, Second International Conference on Computational Intelligence, Robotics and Autonomous Systems, Centre for Intelligent Control, National University of Singapore, SINGAPORE, pp. 1-6.
  95. Biddargaddi, N., Chetty, M., 2003, An incremental constructive layer algorithm for controller design’,, Design and application of hybrid intelligent systems, ISBN 1586033948, 9781586033941, pp. 58-65.

Short Papers

  1. Jaskaran Gill, Madhu Chetty, Adrian Shatte, Jennifer Hallinan, 2021, “Use of known gene-gene interactions in S-system based GRN inference” IEEE International Conference on International Conference on Bioinformatics and Computational Biology (CIBCB2021), ISBN (e-version): 978-0-908026-67-8, DOI: https://doi.org/10.25955/1a39-wk54
  2. [144] Hasini Gamage, Madhu Chetty, Adrian Shatte, Jennifer Hallinan, 2021 “Efficient Ensemble Feature Selection Based Boolean Modelling For Genetic Network Inference” IEEE International Conference on Bioinformatics and Computational Biology (CIBCB2021), ISBN (e-version): 978-0-908026-67-8, DOI: https://doi.org/10.25955/1a39-wk54 (Best short paper award

Other Research Outputs - Editorials:

  1. Jennifer Hallinan, Madhu Chetty, Gonzalo Ruz Heredia, Adrian Shatte and Suryani Lim, Proceedings of the IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology, 2021, EDITORIAL
  2. Madhu Chetty, J Hallinan, G Ruz, and A Wipat, Special Issue of Computational Intelligence in Bioinformatics and Computational Biology, Elsevier’s Biosystems journal, EDITORIAL, in process, 2022
  3. Madhu Chetty, Jennifer Hallinan, Suryani Lim and Adrian Shatte and Cameron Foale of Supplemental Proceedings of the IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology, 2021, EDITORIAL
  4. A. Abraham, D Virmani, O. Castillo, M. Chetty, B Suri, C Gupta, P Girdhar (Eds) 2020, Special issue in Journal of Information & Optimization Sciences (JIOS): ISSN: 0252-2667; Taylor & Francis, 2020, EDITORIAL
  5. Madalina Drugan, Marco Wiering, Peter Vamplew, Madhu Chetty,  ‘Multiobjective Reinforcement Learning: Theory and Applications’, in Special Issue on Multiobjective Reinforcement Learning: Theory and Applications, Elsevier Neurocomputing journal, https://doi.org/10.1016/j.neucom.2017.06.020, Vol 263, 2017
  6. Ahmad, S., Chetty, M., Schmidt, B., Pattern recognition in bioinformatics’, Pattern Recognition Letters, EDITORIAL, Volume 31, Issue 14, Pages 2071-2072, 2010.
  7. M. Chetty, A. Ngom, E Marchiori, ‘Computational Intelligence in Bioinformatics’ Neurocomputing, EDITORIAL, Volume 73, Issue 13-15, pages 2291-2292, 2010.
  8. M Chetty, Alioune Ngom, Shandar Ahmad, “ Pattern Recognition in Bioinformatics”, Springer publisher, Melbourne, EDITORIAL, pp. , 2008.