Discriminating Graph Pattern Mining from Gene Expression Data

2017 ◽  
pp. 31-45
Author(s):  
Fabio Fassetti ◽  
Simona E. Rombo ◽  
Cristina Serrao
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Pattern Mining ◽  
Expression Data ◽  
Graph Pattern

Discriminating graph pattern mining from gene expression data

2016 ◽  
Vol 16 (3) ◽  
pp. 26-36
Author(s):  
Fabio Fassetti ◽  
Simona E. Rombo ◽  
Cristina Serrao
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Pattern Mining ◽  
Expression Data ◽  
Graph Pattern

scholarly journals Extracting regulatory modules from gene expression data by sequential pattern mining

BMC Genomics ◽  
2011 ◽  
Vol 12 (Suppl 3) ◽  
pp. S5 ◽  
Author(s):  
Mingoo Kim ◽  
Hyunjung Shin ◽  
Tae Su Chung ◽  
Je-Gun Joung ◽  
Ju Han Kim
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Pattern Mining ◽  
Sequential Pattern Mining ◽  
Sequential Pattern ◽  
Expression Data ◽  
Regulatory Modules

scholarly journals CLPSO - Fuzzy Frequent Pattern Mining from Gene Expression Data

2012 ◽  
Vol 4 ◽  
pp. 807-811 ◽  
Author(s):  
Shruti Mishra ◽  
Sandeep Ku. Satapathy ◽  
Debahuti Mishra
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Frequent Pattern ◽  
Expression Data

Discovering Knowledge from Local Patterns in SAGE Data

2011 ◽  
pp. 251-267 ◽  
Author(s):  
Bruno Crémilleux ◽  
Arnaud Soulet ◽  
Jiri Kléma ◽  
Céline Hébert ◽  
Olivier Gandrillon
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Pattern Mining ◽  
Expression Data ◽  
Real World Data ◽  
Local Patterns ◽  
Open Issue ◽  
Gene Data ◽  
Sage Data

The discovery of biologically interpretable knowledge from gene expression data is a crucial issue. Current gene data analysis is often based on global approaches such as clustering. An alternative way is to utilize local pattern mining techniques for global modeling and knowledge discovery. Nevertheless, moving from local patterns to models and knowledge is still a challenge due to the overwhelming number of local patterns and their summarization remains an open issue. This chapter is an attempt to fulfill this need: thanks to recent progress in constraint-based paradigm, it proposes three data mining methods to deal with the use of local patterns by highlighting the most promising ones or summarizing them. Ideas at the core of these processes are removing redundancy, integrating background knowledge, and recursive mining. This approach is effective and useful in large and real-world data: from the case study of the SAGE gene expression data, we demonstrate that it allows generating new biological hypotheses with clinical application.

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