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MIND Models in decision making & data @nalysis Enza Messina and Francesco Archetti

MIND Models in decision making & data @nalysis Enza Messina and Francesco Archetti

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Page 1: MIND Models in decision making & data @nalysis Enza Messina and Francesco Archetti

MINDModels in decision making & data @nalysis

Enza Messina and Francesco Archetti

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Page 2: MIND Models in decision making & data @nalysis Enza Messina and Francesco Archetti

Main ActivitiesResearch Areas

o Machine Learning Algorithmso Probabilistic and Relational Modelso Optimization Under Uncertainty

o Multimedia Documento Life Scienceso Ambient Intelligenceo Finance

Applicative Domains

Faculty: Francesco Archetti Enza Messina

Guglielmo LulliPost Doc: Elisabetta FersiniPhD: Federica BargnaOthers: Daniele Toscani

Ilaria GiordaniGaia ArosioLuigi Quarenghi

Page 3: MIND Models in decision making & data @nalysis Enza Messina and Francesco Archetti

Machine Learning and Relational Data

- Traditional learning methods are consistent with the classical statistical inference problem formulation istances are independent and identically distributed (i.i.d.)

aiuto!

ProbabilisticModels

LearningTechniques

SRL

ProbabilisticModels

Relational Representation

LearningTechniques

- but do not reflect the real world! We need a solution able to deal with relationships and

with uncertainty in more general terms

SL

Page 4: MIND Models in decision making & data @nalysis Enza Messina and Francesco Archetti

The World is Uncertain

Graphical Models (here e.g. a Bayesian network) - model uncertainty explicitly by representing the joint distribution

Fever Ache

InfluenzaRandom Variables

Direct Influences

Propositional Model!

Page 5: MIND Models in decision making & data @nalysis Enza Messina and Francesco Archetti

Real-World Data are structured

PatientID Gender Birthdate

P1 M 3/22/63

PatientID Date Physician Symptoms Diagnosis

P1 1/1/01 Smith palpitations hypoglycemic P1 2/1/03 Jones fever, aches influenza

PatientID Date Lab Test Result

P1 1/1/01 blood glucose 42 P1 1/9/01 blood glucose 45

PatientID SNP1 SNP2 … SNP500K

P1 AA AB BB P2 AB BB AA

PatientID Date Prescribed Date Filled Physician Medication Dose Duration

P1 5/17/98 5/18/98 Jones prilosec 10mg 3 months

Non- i.i.d

First-Order Logic / Relational Databases

Page 6: MIND Models in decision making & data @nalysis Enza Messina and Francesco Archetti

Probabilistic Relational Models

Integrate uncertainty with relational model

Convenient language for specifying complex models “Web of influence”: subtle & intuitive reasoning

Framework for incorporating heterogeneous data by connecting related entities (consider also relation uncertainty)

New problems: Relational clustering Collective classification

Open Problems: Inference and Learning

Level

Gene Cluster

LipidHSF

Endoplasmatic

GCN4

Exp. cluster

Exp. type

Heterogeneous Information

Inference

Page 7: MIND Models in decision making & data @nalysis Enza Messina and Francesco Archetti

Uncertainty, Relations, Dynamics

Cau

sal

R

ela

tio

nsh

ips

Struct

. Rel

Sequence(Hidden) Markov Model

Bayes Net DBN

PRM,RBN,SLP…

MRDM,ILP

Relational Markov Model

DPRM

Page 8: MIND Models in decision making & data @nalysis Enza Messina and Francesco Archetti

Some Applications

Page 9: MIND Models in decision making & data @nalysis Enza Messina and Francesco Archetti

Learning Models for Relational Data: Relational Clustering

#origin_ref#destination

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Link♦ document_i

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Document

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Document Analysis

E. Fersini, E. Messina, F. Archetti, “A probabilistic relational approach for web document clustering”, Journal of Information Processing and Management, Vol. 46, no 2, p. 117-130, 2010.

E. Fersini, E. Messina, F. Archetti. “Web page classification: A probabilistic model with relational uncertainty”. In Proc. of the 2010 Conference on Information Processing and Management of Uncertainty, 2010.

E. Fersini, E. Messina, F. Archetti, Probabilistic relational models with relational uncertainty: an early study in web page classification, IEEE WI-IAT Workshop, 2009.

Publications

1. Constraint Learning

2. Objective Function Adaptation Relational Classification:

Probabilistic Relational Models with Relational Uncertainty Conditional Random Fields

E. Fersini, E. Messina, F. Archetti, “Probabilistic relational models with relational uncertainty”, Journal of Information Processing and Management, (second revision).

Submitted

Page 10: MIND Models in decision making & data @nalysis Enza Messina and Francesco Archetti

Document AnalysisE-Forensics JUdicial MAnagement by Digital Libraries Semantics

Information Extraction

Emotion Recognition

Proceedings n° ……..

Accused Name XXXXXX

Witness Name KKKKKK

Prosecutor Name -

Lawyer Name YYYYYYZZZZZZ

Meeting Date 1989

Meeting Location Civitanova Marche

Hearing Summarization

Page 11: MIND Models in decision making & data @nalysis Enza Messina and Francesco Archetti

Document AnalysisE-Forensics

E. Fersini, E. Messina, F. Archetti. “Multimedia Summarization in Law Courts: A Clustering-based Environment for Browsing and Consulting Judicial Folders”. In proc. of the 10th Industrial Conference on Data Mining, 2010.

E. Fersini, G. Arosio, E. Messina, F. Archetti, “Emotion recognition in judicial domain: a multilayer SVM approach, LNAI, in Proc. of the 6th International Conference on Machine Learning and Data Mining, Leipzig, 2009.

E. Fersini, G. Arosio, E. Messina, F. Archetti, D. Toscani. Multimedia Summarization in Law Courts: An Environment for Browsing and Consulting Judicial Folders. In Proc. of the 2nd International Conference on ICT Solutions for Justice, Skopje, 2009.

E. Fersini, F. Callegaro, M. Cislaghi, R. Mazzilli, S. Somaschini, R. Muscillo, D. Pellegrini,. Managing Knowledge Extraction and Retrieval from Multimedia Contents: a Case Study in Judicial Domain. In Proc. of the 2nd International Conference on ICT Solutions for Justice, Skopje, 2009.

G. Felici, E. Fersini, E. Messina, Information extraction through constrained inference in Conditional Random Fields, AIRO 2010, september 2010.

Publications

Submitted Projects

Progetto PONeJRM - electronic Justice Relationship Management

Submitted

E. Fersini, E. Messina, F. Archetti. “Emotional States in Judicial Courtrooms: An Experimental Investigation”. Sumbitted to Journal of Speech Commiunication.

E. Fersini, E. Messina, D. Toscani, F. Archetti, M. Cislaghi. Semantics and machine learning for building the next generation of judicial case and court management systems. Submitted to the Int. Conference on Knowledge Management and Information Sharing

Page 12: MIND Models in decision making & data @nalysis Enza Messina and Francesco Archetti

Life Sciences

Page 13: MIND Models in decision making & data @nalysis Enza Messina and Francesco Archetti

Systems Biology Applications

Regulatory modules

TF

Gene CodingControl

DNA

RNAsingle strand

Transcription +

Human cancer

Gene expressio

n

Drug Activity

Gene drug interactionidentification of a drug treatment for a given cell line based both on drug activity pattern and gene expression profile

Learning gene regulatory networks

Modelling the pharmacology of cancer

Collaborations

Page 14: MIND Models in decision making & data @nalysis Enza Messina and Francesco Archetti

14

Pharmacogenomics Application: Predict drug response to oral anticoagulation therapy (OAT)

Grouping (Profiling) patients based on their clinical and genotypic features in order to suggest doctors the correct drug dosage

Haemorragic riskThrombotic riskData of about 4000 patients:

Clinical and therapeutical data: personal patients data, medical diagnosis, therapy, INR and dosage measurements Genetic data: polymorphism of three genes: CYP2C9, VKORC1 and CYP4F2 that contribute to differences in patients’ response.

In collaboration with

Page 15: MIND Models in decision making & data @nalysis Enza Messina and Francesco Archetti

Publications

E. Fersini, C. Manfredotti, E. Messina, F. Archetti Relational K-Means for Gene Expression Profiles and Drug Activity Pattern Analysis, to appear on Int. Journal of Mathematical Modelling and Algorithms.

F. Archetti, I.Giordani, L. Vanneschi, “Genetic Programming for Anticancer Therapeutic Response Prediction using the NCI-60 Dataset”, Computers & Operations Research, Vol.37, No.8, pp.1395-1405, August 2010.

E. Fersini, I.Giordani, E.Messina, F. Archetti, "Relational Clustering and Bayesian Networks for Linking Gene Expression Profiles and Drug Activity Patterns", International Workshop of Applications of Machine Learning in Bioinformatics (satellite workshop of IEEE International Conference on Bioinformatics and Biomedicine- BIBM, november 2009.

L. Vanneschi , F. Archetti, M. Castelli, I. Giordani, "Classification of Oncologic Data with Genetic Programming," Journal of Artificial Evolution and Applications, vol. 2009, Article ID 848532, 13 pages, 2009. doi:10.1155/2009/848532.

F. Archetti, I.Giordani, L. Vanneschi, “Genetic Programming for QSAR Investigation of Docking Energy”, Applied Soft Computing, Vol. 10, No. 1, pp. 170-182, issn: 1568-4946, Jan 2010.

G. Ogliari, I. Giordani, A. Mihalich, D. Castaldi, A. Di Blasio, A. Dubini, E. Messina, F. Archetti, D. Mari, Nuova classificazione clinica e Farmacogenetica per predire la dinamica dell'inr nell'anziano in tao. Giornale di gerontologia, vol. lvii; p. 495-496, issn: 0017-0305, dicembre 2009

F. Archetti, I. Giordani, E. Messina, G. Ogliari, D. Mari, "A comparison of data mining approaches in the categorization of oral anticoagulant patients", International Workshop of Applications of Machine Learning in Bioinformatics (satellite workshop of IEEE International Conference on Bioinformatics and Biomedicine- BIBM, november 2009

Submitted

F. Archetti, I.Giordani, G.Mauri, E.Messina. “A new clustering approach for learning transcriptional regulatory modules”, submitted to Int. Journal of Data Mining and Bioinformatics, (second revision).

Page 16: MIND Models in decision making & data @nalysis Enza Messina and Francesco Archetti

Projects

Submitted proposals:

Associazione lotta alla trombosi - Call for applications 2010Oral Anticoagulation Therapy in the elderly and womenPartners:

Brunel University, Centre for Intelligent Data AnalysisHarvard Medical School, Biomedical Cybernetics LaboratoryUniv. of Milano, Dept. of Medical Sciences, Geriatrics UnitIst. Clinico Humanitas - Thrombosis Unit (Corrado Lodigiani, MD, PhD)Ist. Auxologico Italiano, IRCCS Centro di Ricerche e Tecnologie Biomediche,

PONHEARTDRIVE Project Coordinator: Calpark – Parco Tecnologico e Scientifico della Calabria

PRINRevealing common patterns among insuline resistance, osteoporosis and chronic inflammatory diseases by using Bayesian Networks.Project Coordinator: Università degli Studi "Magna Graecia" di CATANZARO

Page 17: MIND Models in decision making & data @nalysis Enza Messina and Francesco Archetti

Ambient Intelligence

Page 18: MIND Models in decision making & data @nalysis Enza Messina and Francesco Archetti

Multi-target trackingMulti-target tracking: finding the tracks of an unknown number of moving targets from noisy observations.

Exploiting relations can improve the efficiency of the tracker Monitoring relations can be a goal in itself

We model the transition probability of the system with a RDBN.

In collaboration with

A new representation modelling not only objects but also their relations A new computational strategy based on a family of Sequential Monte Carlo

methods called Particle Filter

Statistical techniques for the detection of anomalous behaviours

Cristina E. Manfredotti, Enza Messina: Relational Dynamic Bayesian Networks to Improve Multi-target Tracking. ACIVS 2009: 528-539.C. Manfredotti, E. Messina, D.J. Fleet, Relations to improve multi-target tracking in an activity recognition system. Proceedings of the International Conference on Imaging for Crime Detection and Prevention, London, 2009.

Publications

Page 19: MIND Models in decision making & data @nalysis Enza Messina and Francesco Archetti

Wireless Sensor Networks Bayesian abstractions for virtual sensing through low cost data aggregation and net-Bayesian abstractions for virtual sensing through low cost data aggregation and net-

wide anomaly detectionwide anomaly detection Modelling Cluster Heads as nodes of a BNModelling Cluster Heads as nodes of a BN Inference to know sensor values also in presence of temporary faults:Inference to know sensor values also in presence of temporary faults:

Lack of communication (sensor failure or sleep)Lack of communication (sensor failure or sleep) Outlier due to sensor malfunctioningOutlier due to sensor malfunctioning

1919

CH1CH2

CH3

CH4

CH5

WSN

BN

sink

F. Archetti, E. Messina, D. Toscani and M. Frigerio - IKNOS – Inference and Knowledge in Networks Of Sensors. International Journal of Sensor Networks (IJSNet), Vol.8 No. 3, 2010

F. Chiti, R. Fantacci, , F. Archetti, E. Messina, D. Toscani, Integrated Communications Framework for Context aware Continuous Monitoring with Body Sensor Networks, IEEE Journal on Selected Areas in Communications - Wireless and Pervasive Communications for Healthcare. Volume 27, Issue 4, 2009., 2009.

D. Toscani, I. Giordani, M. Cislaghi, L. Quarenghi. Querying Sensor Data for Environmental Monitoring. Submitted to International Journal of Sensor Networks (IJSNet), 2010

D. Toscani, I. Giordani, L. Quarenghi, F. Archetti . A software Environment For Supporting Sensor Querying. Submitted to IEEE Sensors 2010 Conference, Hawaii, 2010

Publications

Submitted

Page 20: MIND Models in decision making & data @nalysis Enza Messina and Francesco Archetti

Transportation & Logistics

In collaboration with:

Data Models Decisions

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PRIN MIUREnhancing the European Air Transportation SystemPartners: Università di Padova, Università di Trieste.

Projects

Page 21: MIND Models in decision making & data @nalysis Enza Messina and Francesco Archetti

LENVIS - Localised environmental and health information services for all (EU-FP7)sviluppo di una rete collaborativa di supporto alle decisioni, per lo scambio di informazioni e servizi riguardanti l'ambiente e la salute

Publications

D. Toscani, L. Quarenghi, F.Bargna, F. Archetti, E. Messina, "A DSS for Assessing the Impact of Environmental Quality on Emergency Hospital Admissions", In proceedings of the WHCM 2010 - IEEE Workshop on Health Care Management, February 18-20, 2010 - Venice, Italy.

Ambient IntelligenceProjects

D. Toscani, I. Giordani, F. Bargna, L. Quarenghi, F. Archetti. A software System for Data Integration and Decision Support for Evaluation of Air Pollution Health Impact. Submitted to ICEIS 2010 - 12th International Conference on Enterprise Information Systems. Funchal, Madeira – Portugal, 2010

Submitted

Page 22: MIND Models in decision making & data @nalysis Enza Messina and Francesco Archetti

INSYEME – Integrated Systems for Emergencies (MIUR - FIRB)

GREIS - Gestione del Risparmio Energetico attraverso Informazioni di Sicurezza (MIUR)

In collaboration with SAL Lab.

H-CIM Health Care through Intelligent Monitoring (MIUR) In collaboration withNOMADIS Lab.

Projects

Submitted

FP7 ICT call 6 - STREPOPENCITY Open framework for Transport Demand Management for smart and sustainable

urban mobility in an open and accessible city Project Coordinator: Consorzio Milano Ricerche

In collaboration with SAL Lab. e Imaging & Vision Lab.

FLECS – FLy’s eyes for Collaborative Surveillance – (Progetto PON)

Page 23: MIND Models in decision making & data @nalysis Enza Messina and Francesco Archetti

Financial Time Series

Page 24: MIND Models in decision making & data @nalysis Enza Messina and Francesco Archetti

Hidden var.: Regime

Financial Time Series & Scenario Generation

1( | )

( | )t t

t t

p x x

p z x−Transition Model

Observation Model

Markov Chain

Mixture of Gaussians(Autoregressive Process)

(Autoregressive) Hidden Markov Model

Observations: pricestxtS

tS

Regime Switching Models

t=1 t=2 t=3 t=4

24

Page 25: MIND Models in decision making & data @nalysis Enza Messina and Francesco Archetti

Financial Time Series Extend state space models to more general Relational Dynamic Bayesian Networks to

account not only prices but also, through CPT, “exogenous” economic factors and unstructured information

Algorithms for managing risk tracking portfolio using all available evidence and taking into account all uncertainties

“Markets are good at gathering information from many heterogeneous sources and combining it appropriately, the same we would expect from models”

PRIN 2007 "Modelli probabilistici per la rappresentazione dell’incertezza per la definizione di metodologie di selezione del portafoglio” (Università di Bergamo, Università della Calabria)

Collaboration with Brunel University and CARISMA Research Centre:

Workshop “Application of Hidden Markov Models and Filters to Time Series Methods in Finance” , London, September 2010

Projects & Collaborations

G. Consigli, C. Manfredotti, E. Messina, A sequential learning method for tracking stochastic volatility, EURO XXIV, July 2010, Lisbon 

Publications

Page 26: MIND Models in decision making & data @nalysis Enza Messina and Francesco Archetti

The cooperation network

University of

Toronto

Massachusset

Institute of

Technology

Norwegian University of Science

and Technology

Brunel Univers

ity

Centre of Research and Technology

Hellas

Hungarian Academy of Sciences

CARISMA

Research Center

Harvard Medical School

SB RAS Russia

Aachen University