Discovering Knowledge from Local Patterns in SAGE Data

Author:

Crémilleux Bruno1,Soulet Arnaud2,Kléma Jiri3,Hébert Céline1,Gandrillon Olivier4

Affiliation:

1. Université de Caen, France

2. Université François Rabelais de Tours, France

3. Czech Technical University in Prague, Czech Republic

4. Université de Lyon, France

Abstract

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.

Publisher

IGI Global

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Partial Orders and Logical Concept Analysis to Explore Patterns Extracted by Data Mining;Conceptual Structures for Discovering Knowledge;2011

2. Sequential Patterns to Discover and Characterise Biological Relations;Computational Linguistics and Intelligent Text Processing;2010

3. Recursive Sequence Mining to Discover Named Entity Relations;Lecture Notes in Computer Science;2010

4. Compilation of References;Data Mining and Medical Knowledge Management

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3