; &' A8S3ffff̙̙3f3fff3f3f33333f33333\pwww Ba=(
=h\:#8X@"1Arial1Arial1Arial1Arial1 Arial"$"#,##0_);\("$"#,##0\)!"$"#,##0_);[Red]\("$"#,##0\)""$"#,##0.00_);\("$"#,##0.00\)'""$"#,##0.00_);[Red]\("$"#,##0.00\)7*2_("$"* #,##0_);_("$"* \(#,##0\);_("$"* ""_);_(@_).))_(* #,##0_);_(* \(#,##0\);_(* ""_);_(@_)?,:_("$"* #,##0.00_);_("$"* \(#,##0.00\);_("$"* ""??_);_(@_)6+1_(* #,##0.00_);_(* \(#,##0.00\);_(* ""??_);_(@_) + ) , * `+Proyectos de I+D+i%,,Estancias y sabticos recogid9Tesis DoctoralesF.Artculos en revistasK3Captulos de libros%5Conferencias invitadas en con%7Cursos, seminarios y tutorial%8Informes para las AAPP o sus 9Libros,:Otras Publicaciones9;Ponencia en Congresos,?Creacin de empresas9@KnowHowFAPatentesSBRegistros de Software`CVariedades vegetalesmD
Otros mritoszEPremiosFResponsabilidades %GPersonal para epgrafe de forz
xTtuloDescripcin
InternacionalJCR del ISITtulo de la revistaISSNFactor de impacto JCRInformacin de impactoVolumenDOINmero de revistaDesde la pginaHasta la pginaMesRanking
ParticipantesOtros ParticipantesData Mining Validation of Fluconazole Breakpoints Established by the European Committee on Antimicrobial Susceptibility Testing^European Committee on Antimicrobial Susceptibility Testing (EUCAST) breakpoints classify Candida strains with a fluconazole MIC <= 2 mg/liter as susceptible, those with a fluconazole MIC of 4 mg/liter as representing intermediate susceptibility, and those with a fluconazole MIC > 4 mg/liter as resistant. Machine learning models are supported by complex statistical analyses assessing whether the results have statistical relevance. The aim of this work was to use supervised classification algorithms to analyze the clinical data used to produce EUCAST fluconazole breakpoints. Five supervised classifiers (J48, Correlation and Regression Trees [CART], OneR, Naive Bayes, and Simple Logistic) were used to analyze two cohorts of patients with oropharyngeal candidosis and candidemia. The target variable was the outcome of the infections, and the predictor variables consisted of values for the MIC or the proportion between the dose administered and the MIC of the isolate (dose/MIC). Statistical power was assessed by determining values for sensitivity and specificity, the falsepositive rate, the area under the receiver operating characteristic (ROC) curve, and the Matthews correlation coefficient (MCC). CART obtained the best statistical power for a MIC > 4 mg/liter for detecting failures (sensitivity, 87%; falsepositive rate, 8%; area under the ROC curve, 0.89; MCC index, 0.80). For dose/MIC determinations, the target was > 75, with a sensitivity of 91%, a falsepositive rate of 10%, an area under the ROC curve of 0.90, and an MCC index of 0.80. Other classifiers gave similar breakpoints with lower statistical power. EUCAST fluconazole breakpoints have been validated by means of machine learning methods. These computer tools must be incorporated in the process for developing breakpoints to avoid researcher bias, thus enhancing the statistical power of the model.1(ANTIMICROBIAL AGENTS AND CHEMOTHERAPY 006648044,716
53 72949 2954 ENEROLAutor: Pedro Maria Larraaga Mugica//Autor: Maria Concepcion Bielza Lozoya//Participante: Albert Pahissa Hosp Univ Valle Hebron//Participante: Benito Almirante Hosp Univ Valle Hebron//Participante: Dolores Rodriguez Pardo Hosp Carlos III//Participante: Manuel Cuenca Estrella Inst Salud Carlos III//Participante: Isabel Cuesta Inst Salud Carlos III//Participante: Juan L. Rodriguez Tudela Inst Salud Carlos III//Participante: Fernando Laguna Hosp Carlos III//cEstimation of Distribution Algorithms as Logistic Regression Regularizers of Microarray ClassifiersgThe "large k (genes), small N (samples)" phenomenon complicates the problem of microarray classification with logistic regression. The indeterminacy of the maximum likelihood solutions, multicollinearity of predictor variables and data overfitting cause unstable parameter estimates. Moreover, computational problems arise due to the large number of predictor (genes) variables. Regularized logistic regression excels as a solution. However, the difficulties found here involve an objective function hard to be optimized from a mathematical viewpoint and a careful required tuning of the regularization parameters.$METHODS OF INFORMATION IN MEDICINE 002612701,057
48 3236 241 jAutor: Maria Concepcion Bielza Lozoya//Autor: Pedro Maria Larraaga Mugica//Autor: Victor Robles Forcada//SPredicting citation count of Bioinformatics papers within four years of publicationThis article presents a new approach based on building several prediction models for the Bioinformatics journal. These models predict the citation count of an article within 4 years after publication (global models). To build these models, tokens found in the abstracts of Bioinformatics papers have been used as predictive features, along with other features like the journal sections and 2week postpublication periods. To improve the accuracy of the global models, specific models have been built for each Bioinformatics journal section (Data and Text Mining, Databases and Ontologies, Gene Expression, Genetics and Population Analysis, Genome Analysis, Phylogenetics, Sequence Analysis, Structural Bioinformatics and Systems Biology). In these new models, the average success rate for predictions using the naive Bayes and logistic regression supervised classification methods was 89.4% and 91.5%, respectively, within the nine sections and for 4year time horizon.BIOINFORMATICS 136748034,328
25 243303 3309 DICIEMBRE@Participante: Alfonso Ibaez Universidad Politcnica de Madrid//Edicin del LibroEditorial del LibroISBNSerieTtulo del LibroDesde pginaHasta pginaCProbabilistic Graphical Markov Model Learning: An Adaptive StrategyIn this paper an adaptive strategy to learn graphical Markov
models is proposed to construct two algorithms. A statistical model
complexity index (SMCI) is defined and used to classify models in complexity
classes, sparse, medium and dense. The first step of both algorithms
is to fit a tree using the Chow and Liu algorithm. The second step
begins calculating SMCI and using it to evaluate an index (EMUBI)
to predict the edges to add to the model. The first algorithm adds the
predicted edges and stop, and the second, decides to add an edge when
the fitting improves. The two algorithms are compared by an experimental
design using models of different complexity classes. The samples to
test the models are generated by a random sampler (MSRS). For the
sparse class both algorithms obtain always the correct model. For the
other two classes, efficiency of the algorithms is sensible to complexity.0Springer9783642052576(Lecture Notes in Artificial Intelligence/MICAI 2009: Advances in Artificial Intelligence225236Participante: Eunice PoncedeLen Autonomous University of Aguascalientes//Participante: Elva Daz Autonomous University of Aguascalientes//Nombre congresoTipo de participacinLugar del congreso RevisoresISBN o ISSNFecha inicio congresoFecha fin congresoTtulo de las actas[Mining Probabilistic Models Learned by EDAs in the Optimization of MultiObjective ProblemsOne of the uses of the probabilistic models learned by estimation
of distribution algorithms is to reveal previous unknown
information about the problem structure. In this paper
we investigate the mapping between the problem structure
and the dependencies captured in the probabilistic models
learned by EDAs for a set of multiobjective satisfiability
problems. We present and discuss the application of different
data mining and visualization techniques for processing
and visualizing relevant information from the structure of
the learned probabilistic models. We show that also in the
case of multiobjective optimization problems, some features
of the original problem structure can be translated to the
probabilistic models and unveiled by using algorithms that
mine the model structures.p11th Anual Genetic and Evolutionary Computation Conference (GECCO2009) Associaton for Computing Machinery (ACM)960Montreal (Canad)9781605583259
08/07/2009
12/07/2009445452cProceedings of the 11th Annual Conference on Genetic and Evolutionary Computation (GECCO2009), ACMlAutor: Roberto Santana Hermida//Autor: Maria Concepcion Bielza Lozoya//Autor: Pedro Maria Larraaga Mugica//?Participante: Jos A. Lozano University of the Basque Country//DVariable Selection in Local Regression Models via an Iterative LASSO7The Eight Workshop on Uncertainty Processing (WUPES'09)Liblice (Repblica Checa)9788024515434
19/09/2009
23/09/20092372506The Eight Workshpo on Uncertainty Processing (WUPE'09)Participante: Diego Vidaurre //No<$mbre Apellidos categoraentidadTutorPILAR
FLORES ROMERO'CONTRATADO CON CARGO A PROYECTO UPMOTTPedro Maria Larraaga MugicaRUBENARMAANZAS ARNEDILLOROBERTOSANTANA HERMIDASANTIAGOMUELAS PASCUAL
BECARIO P.I.FHANEN
BORCHANI .PABLO
MRQUEZ NEILAz] I
A W&4#P!"7&'< ))O 2*
dMbP?_*+%"d,,??U}
'>@
dMbP?_*+%"d,,??U}
'>@
dMbP?_*+%"d,,??U}
'>@
3
dMbP?_*+%"d,,??U}
'
!
"
#
$
%
&
'
(
)
*
+
,

.
/
0
1
2
3<>@
5
dMbP?_*+%"d,,??U}
'
4
5
6
7
8
9
:
;
<
=
>
?
@
A
B
C
D>@
dMbP?_*+%"d,,??U}
'>@
dMbP?_*+%"d,,??U}
'>@
dMbP?_*+%"d,,??U}
'>@
dMbP?_*+%"d,,??U}
'>@
dMbP?_*+%"d,,??U}
'>@
>
dMbP?_*+%"d,,??U}
'
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T
U
V
W
X
Y
Z
Z
[
P
\
]
^
_
`
a
b
c
(>@
dMbP?_*+%"d,,??U}
'>@
dMbP?_*+%"d,,??U}
'>@
dMbP?_*+%"d,,??U}
'>@
dMbP?_*+%"d,,??U}
'>@
dMbP?_*+%"d,,??U}
'>@
dMbP?_*+%"d,,??U}
'>@
dMbP?_*+%"d,,??U}
'>@
dMbP?_*+%"d,,??U}
'>@
J
dMbP?_*+%"d,,??U}
'
d
e
f
g
h
i
j
k
(
l
m
n
k
(
l
o
p
k
(
l
q
r
s
(
l
t
u
s
(
l
v
w
s
(
lvxFFFFFF>@
Root EntryWorkbook1K
!"#$%