Paper Info
Title
Knowledge-based integrative framework for hypothesis formation in biochemical networks
Abstract
The current knowledge about biochemical networks is largely incomplete. Thus biologists constantly need to revise or extend existing knowledge. These revision or extension are first formulated as theoretical hypotheses, then verified experimentally. Recently, biological data have been produced in great volumes and in diverse formats. It is a major challenge for biologists to process these data to reason about hypotheses. Many computer-aided systems have been developed to assist biologists in undertaking this challenge. The majority of the systems help in finding “pattern” in data and leave the reasoning to biologists. Few systems have tried to automate the reasoning process of hypothesis formation. These systems generate hypotheses from a knowledge base and given observations. A main drawback of these knowledge-based systems is the knowledge representation formalism they use. These formalisms are mostly monotonic and are now known to be not quite suitable for knowledge representation, especially in dealing with incomplete knowledge, which is often the case with respect to biochemical networks. We present a knowledge based framework for the general problem of hypothesis formation. The framework has been implemented by extending BioSigNet-RR. BioSigNet-RR is a knowledge based system that supports elaboration tolerant representation and non-monotonic reasoning. The main features of the extended system include: (1) seamless integration of hypothesis formation with knowledge representation and reasoning; (2) use of various resources of biological data as well as human expertise to intelligently generate hypotheses. The extended system can be considered as a prototype of an intelligent research assistant of molecular biologists. The system is available at http://www.biosignet.org.
Year
DOI
Venue
2005
10.1007/11530084_11
DILS
Keywords
Field
DocType
existing knowledge,knowledge representation formalism,current knowledge,biochemical network,computer-aided system,extended system,knowledge representation,incomplete knowledge,knowledge base,biological data,knowledge-based integrative framework,hypothesis formation,knowledge based system
Procedural knowledge,Data mining,Knowledge representation and reasoning,Domain knowledge,Computer science,Knowledge-based systems,Model-based reasoning,Knowledge extraction,Knowledge base,Database,Open Knowledge Base Connectivity
Conference
Volume
ISSN
ISBN
3615
0302-9743
3-540-27967-9
Citations 
PageRank 
References 
2
0.43
26
Authors
4
Name
Order
Citations
PageRank
Nam Tran11157.51
Chitta Baral22353269.58
Vinay J. Nagaraj3101.16
Lokesh Joshi4142.28