Text Mining Using JBoss Rules

Text Mining Using JBoss Rules with a BioMedical Example Mark Maslyn Consultant [email_address] To Be Presented 2/2/2010 at Denver Open Source User Group

Acknowledgements
Text Mining Group at the CU Center for Compuatational Pharmacology
Gene Name List From Cytoscape (2007)
23941 Genes
Action Verbs From Blaschke, et al (1999)
Sample Rule From Bali (2009)
Diagram From Bolouri (2008)

What Are Proteins ? Chains of Amino Acids that fold into unique shapes that determine what other proteins will interact with them. Diagram From WikiMedia Commons

Two Proteins Binding Together Diagram From WikiMedia Commons

Interacting Proteins Form New Molecules + + Substrate Enzyme Enzyme Product

Protein Interactions Form Networks Start 1 st Level 2 nd Level From Bolouri (2008) – Used By Permission

Chemical Feedback Loop To Keep Glucose Concentration Constant Glucose ( Sugar ) Too Little Too Much Glycogen ( Fat ) Prot > Prot > Prot Prot < Prot < Prot

Finding Protein / Protein Interactions is the Holy Grail of Pharmacology They Can Lead to New Treatments Image From WikiMedia Commons

Where Do I Get the Data ?

The Problem is: There's Too Much Data 2,000 New References Every Day

The Solution :
Text Mining PubMed to Automatically Extract Information

Two Standard Approaches to Text Mining
ABSTRACTIVE: Statistical methods including Co-Occurrence modeling
EXTRACTIVE: Rules Based using Rule Engines such as JBoss Rules

JBoss Rules !
Drools open source Java project became part of JBoss with version 4.x Current version is 5.x
Rules use Java like syntax
Added capabilities not commonly found in most Rules Engines

Rule Syntax
package com.rules;
import com.bank.Account;
rule "low balance"
when
Account( balance < 100)
then
System.out.println("Balance is less than $100")
end
From Bali(2009)

Example Production Rule (BN Format) with Expected Order S p1 a p2 Where : p1 and p2 = different protein names (e.g. p53, BRCA1, etc) a = action verb (e.g. regulate, interact, modulate, bind, etc)

Word Mapping and Filtering
Case changes – everything goes to lower case
Handle variations of action verbs (e.g. activates, activated, activation)
Removal of "stop" words (e.g. the, this, is, etc.)
Process a single sentence at a time

Text Mining Flow Chart Retrieve Parse Filter and TransformKeywords Rules to Evaluate Output

Cytoscape One Level Network Diagram Statistics: 200 References 7 Unique Links One Level Tree

Cytoscape Two Level Network Diagram Statistics: 1600 References 25 Unique Links Two Level Tree

Further Information Mark Maslyn: [email_address] http://www.slideshare.net/mmaslyn/text-mining-using-jboss-rules-2773851

Text Mining Using JBoss Rules

by Mark Maslyn on Dec 24, 2009

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