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Mining Large Negative Correlations for High-dimensional Contrasting Analysis

Funding: 2013: $105,000
2014: $105,000
2015: $110,000

Project Member(s): Li, J., Catchpoole, D.

Funding or Partner Organisation: Australian Research Council (ARC Discovery Projects)
National University of Singapore
Westmead Hospital (Westmead Hospital Partner Funds)

Start year: 2013

Summary: Negatively correlated variable groups are studied in many high-dimensional data mining problems. However, the lack of efficient methods for the discovery of this new type of correlation severely limits its application, for example, in gene group contrasting analysis, in financial portfolio construction, and in coupling behavior detection. This project will accomplish scalable algorithms to tackle the exponential complexity, and will develop and establish statistical theories to evaluate and rank the correlations discovered from real-life data sets. The research outcome can advance the knowledge base of data mining substantially, and will enable smart information use in bioinformatics and broadly in finance and social network data analysis.


Ghosh, S, Li, J, Cao, L & Ramamohanarao, K 2017, 'Septic shock prediction for ICU patients via coupled HMM walking on sequential contrast patterns.', Journal of Biomedical Informatics, vol. 66, pp. 19-31.
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Liu, Y, Peng, H, Wong, L & Li, J 2017, 'High-speed and high-ratio referential genome compression.', Bioinformatics, vol. 33, no. 21, pp. 3364-3372.
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Zheng, Y, Ji, B, Song, R, Wang, S, Li, T, Zhang, X, Chen, K, Li, T & Li, J 2016, 'Accurate detection for a wide range of mutation and editing sites of microRNAs from small RNA high-throughput sequencing profiles', NUCLEIC ACIDS RESEARCH, vol. 44, no. 14.
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Liu, Q, Ren, J, Song, J & Li, J 2015, 'Co-Occurring Atomic Contacts for the Characterization of Protein Binding Hot Spots.', PLoS ONE, vol. 10, no. 12, pp. 1-18.
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Song, R, Liu, Q, Hutvagner, G, Hung, N, Ramamohanarao, K, Wong, L & Li, J 2014, 'Rule discovery and distance separation to detect reliable miRNA biomarkers for the diagnosis of lung squamous cell carcinoma', BMC GENOMICS, vol. 15.
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Li, Z, He, Y, Liu, Q, Zhao, L, Wong, L, Kwok, CY, Nguyen, HT & Li, J 2013, 'Structural analysis on mutation residues and interfacial water molecules for human TIM disease understanding', BMC Bioinformatics, vol. 14, no. S16, pp. 1-15.
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Keywords: data mining,bioinformatics

FOR Codes: Pattern Recognition and Data Mining, Application Tools and System Utilities, Bioinformatics