Jinyan Li
Jinyan Li
Professor of Data Science, University of Technology Sydney
Verified email at uts.edu.au
Cited by
Cited by
Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling
EJ Yeoh, ME Ross, SA Shurtleff, WK Williams, D Patel, R Mahfouz, ...
Cancer cell 1 (2), 133-143, 2002
Efficient mining of emerging patterns: Discovering trends and differences
G Dong, J Li
Proceedings of the fifth ACM SIGKDD international conference on Knowledge …, 1999
A comparative study on feature selection and classification methods using gene expression profiles and proteomic patterns
H Liu, J Li, L Wong
Genome informatics 13, 51-60, 2002
CAEP: Classification by aggregating emerging patterns
G Dong, X Zhang, L Wong, J Li
International Conference on Discovery Science, 30-42, 1999
Making use of the most expressive jumping emerging patterns for classification
J Li, G Dong, K Ramamohanarao
Knowledge and Information systems 3 (2), 131-145, 2001
Identifying good diagnostic gene groups from gene expression profiles using the concept of emerging patterns
J Li, L Wong
Bioinformatics 18 (5), 725-734, 2002
Mining border descriptions of emerging patterns from dataset pairs
G Dong, J Li
Knowledge and Information Systems 8 (2), 178-202, 2005
Interestingness of discovered association rules in terms of neighborhood-based unexpectedness
G Dong, J Li
Pacific-Asia Conference on Knowledge Discovery and Data Mining, 72-86, 1998
Simple rules underlying gene expression profiles of more than six subtypes of acute lymphoblastic leukemia (ALL) patients
J Li, H Liu, JR Downing, AEJ Yeoh, L Wong
Bioinformatics 19 (1), 71-78, 2003
Deeps: A new instance-based lazy discovery and classification system
J Li, G Dong, K Ramamohanarao, L Wong
Machine Learning 54 (2), 99-124, 2004
Discovery of significant rules for classifying cancer diagnosis data
J Li, H Liu, SK Ng, L Wong
Bioinformatics 19 (suppl_2), ii93-ii102, 2003
Mining statistically important equivalence classes and delta-discriminative emerging patterns
J Li, G Liu, L Wong
Proceedings of the 13th ACM SIGKDD international conference on Knowledge …, 2007
Maximal biclique subgraphs and closed pattern pairs of the adjacency matrix: A one-to-one correspondence and mining algorithms
J Li, G Liu, H Li, L Wong
IEEE Transactions on Knowledge and Data Engineering 19 (12), 1625-1637, 2007
Instance-based classification by emerging patterns
J Li, G Dong, K Ramamohanarao
European Conference on Principles of Data Mining and Knowledge Discovery …, 2000
Emerging patterns and gene expression data
J Li, L Wong
Genome Informatics 12, 3-13, 2001
Minimum description length principle: Generators are preferable to closed patterns
J Li, H Li, L Wong, J Pei, G Dong
AAAI, 409-414, 2006
The long noncoding RNA MALAT1 promotes tumor-driven angiogenesis by up-regulating pro-angiogenic gene expression
AE Tee, B Liu, R Song, J Li, E Pasquier, BB Cheung, C Jiang, ...
Oncotarget 7 (8), 8663-8675, 2016
An in-silico method for prediction of polyadenylation signals in human sequences
H Liu, H Han, J Li, L Wong
Genome Informatics 14, 84-93, 2003
Efficient mining of large maximal bicliques
G Liu, K Sim, J Li
International Conference on Data Warehousing and Knowledge Discovery, 437-448, 2006
Disease gene identification by random walk on multigraphs merging heterogeneous genomic and phenotype data
Y Li, J Li
BMC genomics 13 (Suppl 7), S27, 2012
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