TY - GEN
T1 - Local pre-processing for node classification in networks
T2 - application in protein-protein interaction
AU - Foley, Christopher Eric
AU - Al Azwari, Sana Mohammad M
AU - Dufton, Mark
AU - Ross, Isla
AU - Wilson, John
PY - 2013/8/14
Y1 - 2013/8/14
N2 - Network modelling provides an increasingly popular conceptualisation in a wide range of domains, including the analysis of protein structure. Typical approaches to analysis model parameter values at nodes within the network. The spherical locality around a node provides a microenvironment that can be used to characterise an area of a network rather than a particular point within it. Microenvironments that centre on the nodes in a protein chain can be used to quantify parameters that are related to protein functionality. They also permit particular patterns of such parameters in node-centred microenvironments to be used to locate sites of particular interest. This paper evaluates an approach to index generation that seeks to rapidly construct microenvironment data. The results show that index generation performs best when the radius of microenvironments matches the granularity of the index. Results are presented to show that such microenvironments improve the utility of protein chain parameters in classifying the structural characteristics of nodes using both support vector machines and neural networks.
AB - Network modelling provides an increasingly popular conceptualisation in a wide range of domains, including the analysis of protein structure. Typical approaches to analysis model parameter values at nodes within the network. The spherical locality around a node provides a microenvironment that can be used to characterise an area of a network rather than a particular point within it. Microenvironments that centre on the nodes in a protein chain can be used to quantify parameters that are related to protein functionality. They also permit particular patterns of such parameters in node-centred microenvironments to be used to locate sites of particular interest. This paper evaluates an approach to index generation that seeks to rapidly construct microenvironment data. The results show that index generation performs best when the radius of microenvironments matches the granularity of the index. Results are presented to show that such microenvironments improve the utility of protein chain parameters in classifying the structural characteristics of nodes using both support vector machines and neural networks.
KW - network classification
KW - protein structure
KW - computational biology/bioinformatics
KW - health informatics
KW - information systems
KW - communication service
KW - computer applications
UR - http://www.scopus.com/inward/record.url?scp=84885202052&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-40093-3_3
DO - 10.1007/978-3-642-40093-3_3
M3 - Conference contribution book
AN - SCOPUS:84885202052
SN - 9783642400926
T3 - Lecture Notes in Computer Science
SP - 32
EP - 46
BT - Information Technology in Bio- and Medical Informatics
A2 - Bursa, M.
A2 - Khuri, S.
A2 - Renda, M.E.
CY - Heidelberg
ER -