Download Boolean network (BN) has been a powerful tool for system biology

IDENTIFICATION OF BOOLEAN NETWORKS USING
PREMINED NETWORK TOPOLOGY INFORMATION
ABSTRACT
Boolean network (BN) has been a powerful tool for system biology and Boolean
dynamical system. The successful applications of BN include gene regulatory networks, artificial
neural network, social network, multiagent systems, and so on. These applications are based on
the identification of the BNs. But it is difficult to identify the BNs directly due to the lack of
tools for the logical system.
EXISTING SYSTEM
To solve this problem, several approaches were proposed within the framework of
identifying logical relations directly. For example, a general reverse engineering algorithm used
information theoretic principles to reduce the search space. Using the STP, Cheng proved that
the data required could be reduced considerably when some special structure properties of BNs
are known. To the best of our knowledge, the specific methodology to obtain the structural
information from the observed data has not been investigated yet and still remains challenging.
DIS ADVANTAGES

To identify BNs large amount of data is required.
PROPOSED SYSTEM
In this brief, a novel approach is proposed to reduce the data requirement in the
identification of BNs. Instead of removing fabricated dependence from a fully connected
network, our approach mines the topology by adding each true dependence into an empty
topology. First, a matching table is created to reflect the dependence relationships among nodes
and to provide an approach to reduce the comparisons in the identification. Then, a dynamic
locating matching pair (DLMP) approach for extracting location parameters from dynamic time
series is given, which can be regarded as a dynamic extension to the matching table. Next, based
on the pseudo commutative property of the STP, a position-transform mining (PTM) algorithm is
put forward to improve the data utilization. With the determination of the dependence
Further Details Contact: A Vinay 9030333433, 08772261612, 9014123891 #301, 303 & 304, 3rd Floor,
AVR Buildings, Opp to SV Music College, Balaji Colony, Tirupati - 515702 Email:
[email protected] | www.takeoffprojects.
relationships among nodes, the network topology information is obtained. The premined
topology information can be used to identify the BNs.
ADVANTAGES

Reduce the data requirement in the identification of BNs.
MODULES

Matching Table

Dynamic Locating Matching Pairs

Position-Transforms Mining
SYSTEM REQUIREMENTS
H/W System Configuration:Processor
- Pentium –III
RAM
- 256 MB (min)
Hard Disk
- 20 GB
Key Board
-
Standard Windows Keyboard
Mouse
-
Two or Three Button Mouse
Monitor
- SVGA
S/W System Configuration:Operating System
: Windows95/98/2000/XP
Application Server
: Tomcat5.0/6.X
Front End
: HTML, Jsp
Scripts
: JavaScript.
Server side Script
: Java Server Pages.
Further Details Contact: A Vinay 9030333433, 08772261612, 9014123891 #301, 303 & 304, 3rd Floor,
AVR Buildings, Opp to SV Music College, Balaji Colony, Tirupati - 515702 Email:
[email protected] | www.takeoffprojects.
Database
: MySQL 5.0
Database Connectivity
: JDBC
Further Details Contact: A Vinay 9030333433, 08772261612, 9014123891 #301, 303 & 304, 3rd Floor,
AVR Buildings, Opp to SV Music College, Balaji Colony, Tirupati - 515702 Email:
[email protected] | www.takeoffprojects.