Dall’Intelligenza Artificiale alla Systems Medicine

1. Nicola
Ancona
ancona@ba.issia.cnr.it
Bioinformatics
and
Systems
Biology
Lab
Institute
of
Intelligent
Systems
for
Automation,
National
Research
Council
-­‐
CNR,
Bari,
Italy,
05/12/2013

2. Sommario
— Laboratorio
di
Bioinformatics
e
Systems
Biology
@
ISSIA
–
CNR:
genesi
e
costituzione.
— Infrastrutture
di
calcolo.
— Progetti
e
collaborazioni.
— Competenze
maturate.
— Ricerca
corrente.
— Conclusioni.
05/12/2013

3. Laboratorio
di
Bioinforma/ca
e
Systems
Biology
Genesi
Nei
primi
anni
del
2000
la
comunità
scientifica
operante
nei
settori
dell’Intelligenza
Artificiale
e
del
Pattern
Recognition
scopre
che
l’interpretazione
dei
dati
generati
da
high-­‐throughput
technologies
costituisce
una
formidabile
palestra
per
sperimentare
metodi
sviluppati
in
domini
applicativi
quali
la
visione
artificiale
e
l’apprendimento
automatico,
caratterizzati
da
p
>>
n,
nel
settore
della
scienza
della
vita.
05/12/2013

4. Laboratorio
di
Bioinforma/ca
e
Systems
Biology
Il
laboratorio
di
Bioinformatica
e
Systems
Biology
presso
l’ISSIA
–
CNR
di
Bari
è
un
laboratorio
multidisciplinare
in
cui
biotecnologi,
fisici,
ingegneri
e
informatici
affrontano
questioni
rilevanti
proprie
del
settore
della
scienza
della
vita
con
approcci
computazionali.
Il
gruppo
è
principalmente
coinvolto
nell’analisi
ed
integrazione
di
dati
eterogenei
di
grandi
dimensioni,
generati
dalle
moderne
piattaforme
“omiche”,
per
mettere
in
luce
marcatori
genetici
e
per
svelare
meccanismi
molecolari
responsabili
dell’insorgenza
e
progressione
di
patologie
complesse
e
multifattoriali.
05/12/2013

5. Infrastru9ura
HPC
Il
Laboratorio
di
Bioinformatica
e
Systems
Biology
è
dotato
di
un
High
Performance
Computer
Server,
costituito
da
512
cores,
1.5
TB
di
RAM
e
14
TB
di
storage
interconnesso
in
INFINIBAND.
In
particolare:
— 64
Cluster
nodes:
2x
Intel
X5550
Quad
2.66
GHz
-­‐
24
GB
RAM
-­‐
250GB
HD
-­‐
40
Gbit/s
Infiniband
-­‐
2x
1
Gbit/s
Ethernet
— NAS:
2x
2.53
GHz
-­‐
24
GB
RAM
-­‐
14
TB
HD
-­‐
40
Gbit/s
Infiniband
-­‐
2x
1
Gbit/s
Ethernet
— Front-­‐end:
2x
2.53
GHz
-­‐
24
GB
RAM
-­‐
2x
250GB
HD
-­‐
40
Gbit/s
Infiniband
-­‐
2x
1
Gbit/s
Ethernet
— MATLAB
Parallel
and
distributed
HPC
con
600
workers.
05/12/2013

6. Proge=
— 22/02/2012
–
FIRB
Accordi
di
Programma
2011,
Protocollo
RBAP11B2SX,
dal
titolo
—
—
—
—
“Strategie
innovative
ad
alta
tecnologia
per
lo
studio
del
carcinoma
renale.
Uso
degli
“OMICS”
e
della
Biologia
dei
Sistemi
per
lo
sviluppo
dei
nuovi
biomarkers
(CAROMICS)”.
01/12/2011
–Ricerca
Finalizzata
artt
12
e
12
bis
D.Lgs
502/92
e
s.m.i.
Esercizio
Finanziario
2009.
Progetto
ordinario
RF/2009-­‐1471624
dal
titolo
“Chronic
Rejection
in
Kidney
Transplantation:
identification
of
new
diagnostics
targets”.
17/07/2009
–
P.O.
Puglia
2007
–
2013
Fondo
Sociale
Europeo
2007IT051PO005.
Reti
di
Laboratori
Pubblici
di
Ricerca,
Codice
Progetto
44,
dal
titolo
“Costituzione
di
una
rete
regionale
per
gli
approcci
di
biologia
sistemica
nelle
malattie
umane
(BISIMANE)”.
31/11/2006
–
POR
Puglia
2000
–
2006.
Progetto
Strategico
Regionale
PS_012
dal
titolo
“Ricerca
e
sviluppo
di
nuovi
strumenti
bioinformatici
e
biotecnologici
per
l’analisi
del
profilo
di
espressione
genica
e
proteica
del
cancro
e
per
l’identificazione
di
marcatori
biologici
per
la
diagnosi
precoce
delle
neoplasie
colo-­‐rettali,
renali
e
del
cavo
orale”.
2/1/2002
–
02/04/2006
MURST
-­‐
Cluster
C03
-­‐
Calcolo
Avanzato,
Infrastrutture
Informatiche
e
di
Rete
per
i
Laboratori
di
Analisi
del
Gene,
finanziato
dal
MURST.
05/12/2013

7. Collaborazioni
— Renal,
Dialysis
and
Transplantation
Unit,
Department
of
Emergency
—
—
—
—
—
—
and
Organ
Transplantation,
University
of
Bari,
Bari,
Italy.
Laboratory
and
Division
of
Gastroenterology,
IRCCS
‘Casa
Sollievo
della
Sofferenza’
Hospital,
San
Giovanni
Rotondo
–
Foggia,
Italy.
Hematology
Section,
Department
of
Emergency
and
Organ
Transplantation,
University
of
Bari,
Bari,
Italy.
Department
of
Medical
and
Surgical
Sciences,
University
of
Foggia,
Foggia,
Italy.
Institute
of
Sciences
of
Food
Production,
National
Research
Council,
Bari,
Italy.
Institute
of
Molecular
Cancer
Research,
University
of
Zurich,
Winterthurerstrasse
190,
8057
Zurich,
Switzerland.
Institute
for
Genome
Sciences
and
Policy,
Center
for
Interdisciplinary
Engineering,
Medicine
and
Applied
Sciences,
Duke
University,
101
Science
Drive,
Durham,
NC
27708,
USA.
05/12/2013

8. Competenze
In
questi
anni
il
gruppo
ha
sviluppato
modelli
in
silico
per
l’analisi
ed
integrazione
di
dati
di
trascrittomica,
genomica,
proteomica
per
l’identificazione
di
segnali
molecolari,
processi
biologici
e
pathways
deregolati
in
patologie
complesse
e
per
mettere
in
luce
marcatori
responsabili
dell’insorgenza
e
progressione
di
neoplasie:
— statistiche
predittive.
— statistiche
associative.
05/12/2013

9. Classificazione
su
base
molecolare
di
neoplasie
BMC Bioinformatics
BioMed Central
Open Access
Research article
Regularized Least Squares Cancer Classifiers from DNA
microarray data
Nicola Ancona*1, Rosalia Maglietta1, Annarita D'Addabbo1, Sabino Liuni2
and Graziano Pesole2,3
Address: 1Istituto di Studi sui Sistemi Intelligenti per I'Automazione, CNR, Via Amendola 122/D-I, 70126 Bari, Italy, 2Istituto di Tecnologie
Biomediche-Sezione di Bari, CNR, Via Amendola 122/D, 70126 Bari Italy and 3Dipartimento Scienze Biomolecolari e Biotecnologie, Universitá
di Milano, Via Caloria 26, 20133 Milano, Italy
Email: Nicola Ancona* - ancona@ba.issia.cnr.it; Rosalia Maglietta - maglietta@ba.issia.cnr.it; Annarita D'Addabbo - daddabbo@ba.issia.cnr.it;
Sabino Liuni - sabino.liuni@ba.itb.cnr.it; Graziano Pesole - graziano.pesole@unimi.it
* Corresponding author
from Italian Society of Bioinformatics (BITS): Annual Meeting 2005
Milan, Italy, 17–19 March 2005
Published: 1 December 2005
</supplement>
<supplement> <title> <p>Italian Society of Bioinformatics (BITS): Annual Meeting 2005</p> </title> <editor>Rita Casadio, Alessandro Guffanti, Manuela Helmer-Citterich, Giancarlo Mauri, Luciano Milanesi, Graziano Pesole, Cecilia Saccone and Giorgio Valle</editor> <note>Research articles</note>
BMC Bioinformatics 2005, 6(Suppl 4):S2
Abstract
doi:10.1186/1471-2105-6-S4-S2
05/12/2013
Background: The advent of the technology of DNA microarrays constitutes an epochal change

10. size is adequate for accurate and significant prediction estimates. The strength of this methodology, in contrast to
classical statistical methods, is that it accounts simultaneously for the effect of several genetics markers and their
possible interactions. The findings of this study show that RLS methodology is able to increase the diagnostic
accuracy of CD prediction by contemporary evaluation of a large number of gene polymorphisms. This approach
may be particularly useful in large-scale population screening programs, and when evaluating large datasets of gene
polymorphisms (i.e. chips, microarrays). Moreover, it could shed more light on possible candidate genes with a weak
genetic contribution, and for evaluating gene-gene and gene-phenotype interactions by analyzing populations with
doi: 10.1111/j.1469-1809.2007.00349.x
a reasonably small sample size.
Predizione
del
morbo
di
Crohn
Regularized Least Squares Classifiers may Predict
Keywords: SingleCrohn’s Disease from Profiles of Single Nucleotide
Nucleotide Polymorphisms, Crohn’s disease, CARD15, prediction
Polymorphisms
It has recently been recognized that CD is a complex
genetic disorder where susceptibility alleles and environCrohn’s Disease (CD) is an inflammatory bowel disease
mental exposure are required for disease development.
(IBD) that can affect any part of the gastrointestinal tract,
Association studies have proposed several loci and genes
Summary
with a maximum occurrence in teenagers and young
In this paper we focus on the prediction of Crohn’s disease (CD) susceptibility with CD, with NOD2 (NOD2/CARD15)
correlated by analyzing SNP profiles for a
adults from Western number of definedIt is a life-long diseaseWe assess the correlation between genetic markers and the
countries. or suggested gene polymorphisms.
being the best validated gene
phenotype by using well-founded methods and procedures developed in the field of statistical learning theory. To(reviewed in Gaya et al.
which affects about one million people in the world, composed of 178 CD patients and 127 healthy controls.
this end, we use a sample generated by a case-control study
2006). In particular three mutations in this gene are asThe
profile of
with a chronic, relapsinggeneticleast squares each subject is composed (re- disease with a statistically significant accuracy We62% ( pthat
course and(RLS) classifiers predict Crohn’s genetic variants distributed over 11 genes. of find =
complications of 16
regularized
sociated with CD (R702W, G908R, and L1007fsinsC),
viewed in Shanahan, 0.018), significantly increasing the diagnostic accuracy by at least 10% compared to that obtained with the more
2002).
largely confirmed gene involved in CD predisposition, namely and are This also frequentthat our sample ileal disease, young ageCARD15. more demonstrates in cases of
size is adequate for accurate and significant prediction estimates. The strength of this methodology, in contrast to
of-onset, and complications (reviewed in Economou
classical statistical methods, is that it accounts simultaneously for the effect of several genetics markers and their
possible interactions. The findings of this study show that RLS methodology is able to increase theof other candidate genes based
et al. 2004). A number diagnostic
accuracy of CD prediction by contemporary evaluation
∗ Corresponding Author: Ancona Nicola, Istituto di Studi sui of a large number of gene polymorphisms. This approach
on positional or functional plausibility, have been tested,
may be particularly useful in large-scale population screening programs, and when evaluating large datasets of gene
Sistemi Intelligenti per polymorphisms (i.e. chips, microarrays). Moreover, it could shed more light on possible candidate genes with a weak
l’Automazione, CNR, Via Amendola
with interactions by analyzing populations can
genetic contribution, and for evaluating Fax: +39122/D-I, 70126 Bari, Italy. Tel: +39-080-5929428; gene-gene and gene-phenotype conflicting results that with be explained by differa reasonably small sample size.
ent genetic backgrounds and insufficient power of the
080-5929460. E-mail: ancona@ba.issia.cnr.it
Introduction
A. D’Addabbo1 , A. Latiano2 , O. Palmieri2 , R. Maglietta1 , V. Annese2 and N. Ancona1, ∗
1
Istituto di Studi sui Sistemi Intelligenti per l’Automazione, CNR, Via Amendola 122/D-I, 70126 Bari, Italy.
2
U.O. Gastroenterologia, Ospedale CSS-IRCCS, San Giovanni Rotondo, Italy.
Keywords: Single Nucleotide Polymorphisms, Crohn’s disease, CARD15, prediction
C 2007 The Authors
Introduction
Journal compilation C 2007 University College London
05/12/2013
Crohn’s Disease (CD) is an inflammatory bowel disease
(IBD) that can affect any part of the gastrointestinal tract,
with a maximum occurrence in teenagers and young
Annals of Human Genetics (2007) 71,537–549
It has recently been recognized that CD is a complex
genetic disorder where susceptibility alleles and environmental exposure are required for disease development.
Association studies have proposed several loci and genes
537

11. Pathways
deregola/
in
neoplasie
BIOINFORMATICS
ORIGINAL PAPER
Vol. 23 no. 16 2007, pages 2063–2072
doi:10.1093/bioinformatics/btm289
Gene expression
Statistical assessment of functional categories of genes
deregulated in pathological conditions by using microarray data
R. Maglietta1, A. Piepoli2, D. Catalano3, F. Licciulli3, M. Carella4, S. Liuni3, G. Pesole3,5,
F. Perri2 and N. Ancona1,*
1
`
Istituto di Studi sui Sistemi Intelligenti per l’Automazione, CNR, Via Amendola 122/D-I, 70126 Bari, 2Unita Operativa di
Gastroenterologia, IRCCS, ‘Casa Sollievo della Sofferenza’-Ospedale, Viale Cappuccini, 71013 San Giovanni Rotondo
(FG), 3Istituto di Tecnologie Biomediche-Sezione di Bari, CNR, Via Amendola 122/D, 70126 Bari, 4Servizio di Genetica
Medica, IRCCS, ‘Casa Sollievo della Sofferenza’-Ospedale, Viale Cappuccini, 71013 San Giovanni Rotondo (FG) and
5
`
Dipartimento di Biochimica e Biologia Molecolare - Universita di Bari, Via E. Orabona 4, 70126 Bari, Italy
Received on April 5, 2007; revised on May 14, 2007; accepted on May 21, 2007
Advance Access publication May 31, 2007
Associate Editor: Trey Ideker
ABSTRACT
Motivation: A major challenge in current biomedical research is the
identification of cellular processes deregulated in a given pathology
through the analysis of gene expression profiles. To this end,
05/12/2013
predefined lists of genes, coding specific functions, are compared
with a list of genes ordered according to their values of differential
belonging to two distinct categories (e.g. diseased patients versus
healthy controls, or patients in two different stages of the same
pathology) are collected (Alon et al., 1999; Barrier et al., 2006).
Successively, suitable univariate statistics are used for finding
those genes which are differentially expressed in the experimental conditions analyzed (Golub et al., 1999; Guyon et al.,

12. Gene
set
enrichment
analysis
BMC Bioinformatics
BioMed Central
Open Access
Research article
Comparative study of gene set enrichment methods
Luca Abatangelo1, Rosalia Maglietta1, Angela Distaso1, Annarita D'Addabbo1,
Teresa Maria Creanza1, Sayan Mukherjee2 and Nicola Ancona*1
Address: 1Istituto di Studi sui Sistemi Intelligenti per l'Automazione, CNR, Via Amendola 122/D-I, Bari, Italy and 2Institute for Genome Science
and Policy, Duke University, Durham, NC, USA
Email: Luca Abatangelo - abatangelo@ba.issia.cnr.it; Rosalia Maglietta - maglietta@ba.issia.cnr.it; Angela Distaso - distaso@ba.issia.cnr.it;
Annarita D'Addabbo - daddabbo@ba.issia.cnr.it; Teresa Maria Creanza - creanza@ba.issia.cnr.it; Sayan Mukherjee - sayan@stat.duke.edu;
Nicola Ancona* - ancona@ba.issia.cnr.it
* Corresponding author
Published: 2 September 2009
BMC Bioinformatics 2009, 10:275
doi:10.1186/1471-2105-10-275
Received: 11 November 2008
Accepted: 2 September 2009
This article is available from: http://www.biomedcentral.com/1471-2105/10/275
© 2009 Abatangelo et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
05/12/2013
Background: The analysis of high-throughput gene expression data with respect to sets of genes
rather than individual genes has many advantages. A variety of methods have been developed for

13. Pathway
anaysis
in
colon
cancer
progression
Maglietta et al. BMC Cancer 2012, 12:608
http://www.biomedcentral.com/1471-2407/12/608
RESEARCH ARTICLE
Open Access
Molecular pathways undergoing dramatic
transcriptomic changes during tumor
development in the human colon
Rosalia Maglietta1, Vania Cosma Liuzzi1, Elisa Cattaneo2, Endre Laczko3, Ada Piepoli4, Anna Panza4,
Massimo Carella5, Orazio Palumbo5, Teresa Staiano6, Federico Buffoli6, Angelo Andriulli4, Giancarlo Marra2†
and Nicola Ancona1*†
Abstract
Background: The malignant transformation of precancerous colorectal lesions involves progressive alterations at
05/12/2013
both the molecular and morphologic levels, the latter consisting of increases in size and in the degree of cellular

14. Riproducibilità
dei
risulta/
Journal of Biomedical Informatics 43 (2010) 397–406
Contents lists available at ScienceDirect
Journal of Biomedical Informatics
journal homepage: www.elsevier.com/locate/yjbin
On the reproducibility of results of pathway analysis in genome-wide
expression studies of colorectal cancers
Rosalia Maglietta a, Angela Distaso a, Ada Piepoli b, Orazio Palumbo c, Massimo Carella c,
Annarita D’Addabbo a, Sayan Mukherjee d, Nicola Ancona a,*
a
Istituto di Studi sui Sistemi Intelligenti per l’Automazione, CNR, Via Amendola 122/D-I, Bari, Italy
Unità Operativa di Gastroenterologia, IRCCS, ‘‘Casa Sollievo della Sofferenza”-Ospedale, 71013 San Giovanni Rotondo (FG), Italy
c
Servizio di Genetica Medica, IRCCS, ‘‘Casa Sollievo della Sofferenza”-Ospedale, 71013 San Giovanni Rotondo (FG), Italy
d
Institute for Genome Science and Policy, Duke University, Durham, NC, USA
b
a r t i c l e
i n f o
a b s t r a c t
Article history:
Received 26 June 2009
Available online 29 September 2009
Keywords:
Bioinformatics
Microarray data
Pathway
Colorectal cancer
One of the major problems in genomics and medicine is the identification of gene networks and pathways
deregulated in complex and polygenic diseases, like cancer. In this paper, we address the problem of
assessing the variability of results of pathways analysis identified in different and independent genome
wide expression studies, in which the same phenotypic conditions are assayed. To this end, we assessed
the deregulation of 1891 curated gene sets in four independent gene expression data sets of subjects
affected by colorectal cancer (CRC). In this comparison we used two well-founded statistical models
for evaluating deregulation of gene networks. We found that the results of pathway analysis in expression studies are highly reproducible. Our study revealed 53 pathways identified by the two methods in
all the four data sets analyzed with high statistical significance and strong biological relevance with
the pathology examined. This set of pathways associated to single markers as well as to whole biological
processes altered constitutes a signature of the disease which sheds light on the genetics bases of CRC.
Ó 2009 Elsevier Inc. All rights reserved.
05/12/2013
1. Introduction
two distinct statistical methods: Statistical Analysis of Microarray
(SAM) [7] and Mixed Model Analysis (MMA) [8]. Both methods

15. RS-­‐SNP:
GWAS
in
morbo
di
Crohn
D’Addabbo et al. BMC Genomics 2011, 12:166
http://www.biomedcentral.com/1471-2164/12/166
SOFTWARE
Open Access
RS-SNP: a random-set method for genome-wide
association studies
Annarita D’Addabbo1, Orazio Palmieri2, Anna Latiano2, Vito Annese2, Sayan Mukherjee3 and Nicola Ancona1*
Abstract
Background: The typical objective of Genome-wide association (GWA) studies is to identify single-nucleotide
polymorphisms (SNPs) and corresponding genes with the strongest evidence of association (the ‘most-significant
SNPs/genes’ approach). Borrowing ideas from micro-array data analysis, we propose a new method, named RS-SNP,
for detecting sets of genes enriched in SNPs moderately associated to the phenotype. RS-SNP assesses whether
the number of significant SNPs, with p-value P ≤ a, belonging to a given SNP set S is statistically significant. The
rationale of proposed method is that two kinds of null hypotheses are taken into account simultaneously. In the
first null model the genotype and the phenotype are assumed to be independent random variables and the null
distribution is the probability of the number of significant SNPs in S greater than observed by chance. The second
null model assumes the number of significant SNPs in S depends on the size of S and not on the identity of the
SNPs in S . Statistical significance is assessed using non-parametric permutation tests.
Results: We applied RS-SNP to the Crohn’s disease (CD) data set collected by the Wellcome Trust Case Control
Consortium (WTCCC) and compared the results with GENGEN, an approach recently proposed in literature. The
enrichment analysis using RS-SNP and the set of pathways contained in the MSigDB C2 CP pathway collection
highlighted 86 pathways rich in SNPs weakly associated to CD. Of these, 47 were also indicated to be significant
by GENGEN. Similar results were obtained using the MSigDB C5 pathway collection. Many of the pathways found
to be enriched by RS-SNP have a well-known connection to CD and often with inflammatory diseases.
Conclusions: The proposed method is a valuable alternative to other techniques for enrichment analysis of SNP
sets. It is well founded from a theoretical and statistical perspective. Moreover, the experimental comparison with
GENGEN highlights that it is more robust with respect to false positive findings.
Background
The objective of genome-wide association studies
05/12/2013
(GWAS) is to identify genetic variants, a subset of single
nucleotide polymorphisms (SNPs), associated with the
onset and progression of complex disease phenotypes at
For polygenic diseases focusing the analysis on only the
most significant SNPs is particularly problematic as no
particular gene may have a large effect [1] but genic
regions weakly associated to the phenotype are important when susceptibility is conferred by a large number

16. Inferring
gene
regulatory
networks
from
expression
profiles
05/12/2013

17.
The
Gaussian
concentration
graph
models
to
describe
interac/ons
between
genes
in
a
Gene
Regulatory
Network
(GRN).
G
=
(V,E)
GRAPH
VERTICES
EDGES
Genes
Condi0onal
dependencies
ABSENCE
OF
EDGE
The
covariance
selec0on
models:
a
zero
entry
in
the
concentra0on
matrix
indicates
the
condi0onal
independence
between
the
two
random
normal
variables.
Dempster,
1972
Cox
&
Wermuth,
1996.

18. APPLICATIONS
OF
L2C
ON
REAL
DATA
The
isoprenoid
biosynthesis
pathways
in
A.
thaliana
Evidence
of
two
modules
(plastidial
and
cytosolic
pathways)
with
strongly
interconnected
and
positively
correlated
genes
within
them.
Identification
of
negative
correlated
nodes
as
candidates
for
the
cross-­‐talk
between
the
two
pathways:
HMGS
and
HDS.
A
signature
of
HRAS
oncogene
in
human
cell
cultures
(Bild
et
al.,
2006)
Identification
of
a
common
transcriptional
regulator
of
the
direct
interactors
of
Ras:
RREB1

19. Ruolo
di
elemen/
non
codifican/
nello
studio
delle
patologie
complesse
Utilizzo
di
miRNA
circolanti
come
potenziali
biomarcatori
per
la
diagnostica
precoce
del
cancro
colorettale.
Studio
del
ruolo
dei
long
non-­‐coding
RNA
come
modulatori
dell’attività
dei
miRNA
nel
controllo
dell’insorgenza
di
neoplasie.
05/12/2013

20. Systems
medicine
Il
principale
obiettivo
è
affrontare
e
risolvere
questioni
clinicamente
rilevanti.
In
particolare,
si
occupa
di:
— Fornire
spiegazioni
dei
meccanismi
patologici;
— Utilizzare
il
sangue
come
una
finestra
diagnostica
per
monitorare
lo
stato
di
salute
di
un
individuo;
— Catalogare
e
distinguere
sottotipi
di
patologie
complesse
al
fine
di
a)
studiare
i
meccanismi
biologici
responsabili
della
loro
insorgenza
e
progressione,
b)
individuare
appropriati
farmaci;
05/12/2013

21. Systems
medicine
— Fornire
nuovi
approcci
per
la
scoperta
di
target
farmaceutici;
— Generare
metriche
per
valutare
lo
stato
di
salute
di
un
individuo.
Interessante
il
consorzio
Coordinating
Action
Systems
Medicine
www.casym.eu
sviluppato
nell’ambito
del
FP7-­‐Directorate-­‐General
for
Research
and
Innovation
of
the
European
Commission.
05/12/2013

22. Conclusioni
Siamo
disponibili
a:
— nuove
collaborazioni
con
gruppi
di
ricerca
operanti
sul
territorio;
— affrontare
nuove
problematiche;
— partecipare
a
nuove
iniziative
di
ricerca
in
ambito
nazionale
ed
internazionale.
05/12/2013