BioAgent - UniCam - Computer Science Divisioncomputerscience.unicam.it/merelli/copenhagen.pdf · An...
Transcript of BioAgent - UniCam - Computer Science Divisioncomputerscience.unicam.it/merelli/copenhagen.pdf · An...
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BioAgent
Emanuela Merelli
Informatics and BioScience Group
An Intelligent Mobile Agents platform for BioData manipulation
Copenhagen July 2001
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University of Camerino
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Outline
• Motivations
• Main choices in BioAgent
• System’s architecture
• BioAgent model
• Preliminary results
• On-going work
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Motivations λ Electronic diagnostic tool
• Significant amount of data (ORFs, Experiments, …) disseminated and duplicated in a myriad of different dbs and repositories
• A lot of dedidated tools with different access modes
• Several actors: researchers, physicians, lab-technicians …
• …
λ Our 1st goals
• define a general Open-Source Intelligent Mobile Agent platform to support “biological data analysis”
• define a declarative language to specify BioAgents
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What is an agent? An intelligent mobile agent is a program that can autonomously move between nodes by preserving the status and by managing information (i.e. knowledge) useful to perform its mission
Any agent is characterized by the following features
Moving capability
Communication capability
Cloning capability State-preserving capability
Knowledge management capability
a mobile agent can have a sort of “intelligence”. An agent can decide whether or not to undertake an action. It can represent and dynamically enrich its knowledge by using a descriptive language.
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How it works …
Agent Platform Agent Platform
EnsemBL NCBI
Single agent
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How it works …
Agent Platform Agent Platform
XML XML
Multi agent
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Benefits of mobile agents
• Asynchronous task execution
• Reduction of network traffic and client processing
• Robustness: reduction of dependence of network availability and client/server availability
• Load-balance • Automation of distributed task processing
• Decentralized -local task processing • Flexibility: on demand software distribution
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Problems • Security
λ Malicious host, malicious agent
• Task execution control λ Location and status of agent may be unknown
• Fault management λ Orphan detection, data inconsistency
• Performance λ Interpreted code is slow
• Heterogeneity of hosts • Code transfer overhead
• Access to existing services (e.g. CORBA)
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Requirements
• Agent execution
λ Agent creation, execution, deletion, execution control, task control
• Agent mobility
λ Remote execution, migration, naming, addressing, location, domains
• Agent communication
λ Inter-agent, user/agent, naming, addressing, location of peers
• Agent system security
λ System protection (agent = virus?), agent communication
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host host
System’s architecture
Agent platform
Agent
Operating system
Agent
Hardware
host
…
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Mobile agent platforms
• Aglets: www.tri.ibm.co.jp/aglets
• D’agents: agents.cs.dartmputh.edu
• Grasshoper: www.ikv.de
• Voyager: www.objectspace.com
• Macondo: www.cs.unibo.it/~ciancarini
• Others: Amates, Mole
• Mobile agent platforms are implemented in Java
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Main choices for BioAgent
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Choice 1: all XML
• It’s a W3C standard for data transfer
• Many tools are or will be available
• It’s easy to use
• …
• Bio data DTDs are under definition
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Choice 2: all declarative
• Fast deployment
• Easy administration λ e.g., dynamic change of rules, experiment
categories …
• Automatic verification λ e.g., correctness of protocols
λ e.g., fairness of an extraction system
• etc.
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Choice 3: all autonomous
• Each node is an autonomous elaborative unit
• Each node can have its operating system
• Each node can have a DBMS and/or XML repository
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Choice 4: all Java
• Each node must have a JVM λ Hardware independent (runs on Windows, Unix,
MacOS, PDA, …)
λ Object oriented
λ Ubiquitous (Java does everything, everyone does Java)
λ Network centric • Code mobility (applets, servlets, class loading,
serialisability
• Network communication (TCP/UDP sockets)
• Security (secure sockets, secure manager)
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BioAgent model
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BioAgent model
• Basic features λ comunication,
λ memory,
λ cloning,
λ moving
λ knowledge mng • Application features λ Mission λ Working Tools λ Knowledge (domain)
ability to move the code from one site to another
ability to exchange information with human customer or communicate and synchronize with others agents ability to instance a copy of itself
ability to freeze its status before to move
ability to believe, decide and enrich
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Who will use and specify an agent?
• bioinformatics,
• biochemical researchers,
• physicians,
• lab-technicians,
• biologists
• …
Are they able to program in Java? Must they learn it?
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BioAgent Intelligent Mobile Agent platform for Biology
BioAgent is an abstract machine able to interpreter the application agent instances specified and activated by using a declarative language
• High accessibility
• High modularity • No propriety protocols
• Secure
• Simplicity
• Portability
• Interface
• Space • Core
• Communication
• Security
Features Modules
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BioAgent_L Biological Agent - Language
BioAgent_L is a declarative language, “easy to use”, for specifying an intelligent mobile agent
DDL + DML
Example: how to specify a new environment
let <envname> be VirtualEnv
with Instruments, Events, Objects, Languges …
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…
Example: how to specify a new agent
create <agent> like <tipo> in <envirnment>
with
missions (m1,m2 …mn)
reactions (r1,r2, … rn)
instruments (s1,s2, … sn)
language (l1,l2, … ln)
agent-status (o1,o2, … on)
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System’s architecture
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BioAgent’s Architecture
host host
BioAgent
Agent platform bacis features
BioAgent
Operating system
host
…
Hardware
BioAgent Interpreter
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Computational Analysis of Biological Data
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Agent’s Missions
• Bio data search
• Bio data integration
• Bio data clustering
• Bio data extraction
• Bio pattern recognition
• Bio knowledge discovering
• Bio data prediction
• …
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Agent’s Working tools • Optimization
λ Combinatorial algorithms
λ Heuristics algorithms
λ CLP: Constraints Logic Programming
λ …
• Classification
λ Neural nets
λ Kohonen self-organizing map
λ …
• Knowledge Discovering
λ Data mining
λ Multidimensional analysis
λ …
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Agent’s Knowledge
• Basic knowledge (well-established)
λ Dtd, DAML+OIL
• Extended knowledge λ new knowledge
• Local knowledge λ any, not in XML format
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Application: analysis of gene expression
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Microarray experiments
“...normally an experiment should include a set of hybridisations which are inter-related and
performed in a limited period of time.”
MIAME (Minimal Information About Microarray Experiments) document by MGED
(Microarray Gene Expression Database group, UK)
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Hybridisation • Each hybridisation is
constituted by a collection of experimental data (spots) usually one spot for each ORF (Open Reading Frame).
• The intensity of each spot quantifies the expression of the related ORF under the chosen experimental conditions
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MicroArray Markup Languages
• MAML proposed by the European Molecular Biology Lab (EMBL) and the European Bioinformatic Institute (EBI) and recently submitted to OMG (Object Management Group)
• GEML (proposed by a public-private coomunity, the GEML community [21]).
• BSML (Bioinformatic Sequence Markup Language) proposed by Visualgenomic, Inc. USA
[20].
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MAML DTD structure
1. Experimental design: the set of the hybridization experiments as a whole;
2. Array design: each used array and each element (spot) on the array;
3. Samples: used samples, the extract preparation and labeling;
4. Hybridizations: procedures and parameters;
5. Measurements: images, quantitation, specifications;
6. Controls: types, values, specifications.
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Major goals pursued during the analysis of hybridization data
• data mining • model-based/model-free
• functional classification
• clustering
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Clustering
• Clustering using experiments euc. distance
λ (ORF “guilty-by-association”)
λ Present use: discover gene function
• Clustering using ORFs euc. distance λ (transcriptional fingerprint) λ Future use: diagnostic tool
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Data: Biological data related to an “organism”
λ set of inter-related hybridisations
Mission: analysis of hybridisation data
λ Clustering by experiments
λ Clustering by ORF
Tool:
λ Kohonen self-organizing map
Basic Knowledge: MAML DTD
Exteded Knowledge: Kohonen map
BioAgent
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Kohonen Algorithm
1. We define with wij(t) the weights between the input neuron ith and the jth neuron in the map at time t. The weights initial values are random assigned in the [0,1] range.
2. Given an input x0(t), x1(t), ..xn(t), where xi(t) is the ith input
3. Calculate the distance di between input i and each output neuron j dj
2 = Σ (xi (t)-wij(t))2
4. Select neuron with minimum distance, j*
5. Modify the weights of the input neuron i and j* and its neighbours Ni(j*)
wij(t+1) = wij(t) + η(t)(xi(t)-wiJ(t)) for each j in Ni(j*) and 0≤ i ≤n
η(t) is a gain function 0≤ η(t) ≤1
6. Cycle from step 2
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Preliminary results
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Database source (ExpressDB Aach et al. 2000)
• 147 distinct hybridisation experiments
• 6053 ORFs
Agent’s Platform
• Macondo [Ciancarini97] • MJada for agents coordination and
synchronization
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Kohonen algorithm
• Completly implemented in C, next version will be in Java
• 9 clusters
• The algorithm converges in 10000 cycles
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Kohonen map
100 500 5000 10000
The output
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Simple Demo
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Demo for single agent platform
• We simulate four different sites by four DOS windows. Three will be the place with the data repository and the fourth will be the starting and ending point for an agent
• Open 4 DOS windows
• For each window, in C:\macondo run setenv
• I window: in C:\macondo\esempi\place run
java exResourcePlace1 • II window: in C:\macondo\esempi\place run
java exResourcePlace2 • III window: in C:\macondo\esempi\place run
java exResourcePlace3 • IV window: in C:\macondo\esempi\place run
java RunAgent localhost • The result is in C:\macondo\esempio\risultato.res • PROTOTYPE
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On-going work
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On going work • 1st Phase
λ Analysis and design of the BioAgent system λ Mobile agent basic implementation
λ 1st prototype fo Microarray clustering
λ BioAgent to interface EnsEMBL
• 2nd Phase λ Analysis and design of the knowledge system
λ Definition of BioAgent declarative language
λ Implementation of BioAgent_L interpreter
• 3rd Phase λ Validate the system
λ Genaralize the platform so to allow the definition of personal “views” by BioAgent
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21-Jun-01 E. Merelli
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Informatics and BioScience group at Camerino University
Intelligent
Mobile
Agent platform
for
Biodata analysis
Emanuela Merelli Rosario Culmone Leonardo Mariani
Computer Scientists
Mauro Angeletti Biochemistrist
Cristina Marchini Biologist
Lorenzo Moglie, Diego Bonura,
Gloria Rossi Students in Computer Science