His primary scientific interests are in Memetic algorithm, Bioinformatics, Alzheimer's disease, Disease and Artificial intelligence. He performs integrative Memetic algorithm and Job shop scheduling research in his work. When carried out as part of a general Bioinformatics research project, his work on Biological data is frequently linked to work in Scalability, therefore connecting diverse disciplines of study.
His research on Alzheimer's disease also deals with topics like
His primary areas of investigation include Memetic algorithm, Artificial intelligence, Bioinformatics, Data mining and Machine learning. His studies deal with areas such as Theoretical computer science and Metaheuristic as well as Memetic algorithm. His Artificial intelligence study frequently intersects with other fields, such as Pattern recognition.
His Bioinformatics research incorporates themes from Gene expression profiling, Biomarker, Internal medicine, Disease and Computational biology. His Disease study combines topics in areas such as Recall and Neuroscience. His work carried out in the field of Data mining brings together such families of science as Microarray analysis techniques, Quadratic assignment problem, Cluster analysis and Graph.
Pablo Moscato spends much of his time researching Memetic algorithm, Analytics, Artificial intelligence, Data science and Cluster analysis. His study on Memetic algorithm is covered under Local search. He interconnects Tree and Problem domain in the investigation of issues within Local search.
As a part of the same scientific family, Pablo Moscato mostly works in the field of Analytics, focusing on Data analysis and, on occasion, Metaheuristic and Ensemble learning. His study in Metaheuristic is interdisciplinary in nature, drawing from both Predictive analytics, Feature selection and Curse of dimensionality. His Artificial intelligence study incorporates themes from Machine learning, Network alignment and Regression.
His scientific interests lie mostly in Memetic algorithm, Representation, Local search, Theoretical computer science and Data mining. His Memetic algorithm research is multidisciplinary, relying on both Partition and Cluster analysis. He combines subjects such as Label propagation, Mutual information, Complex system, Population structure and Community structure with his study of Cluster analysis.
His Representation study is concerned with the field of Artificial intelligence as a whole. The study incorporates disciplines such as Test, Data collection and Regression in addition to Artificial intelligence. Within one scientific family, Pablo Moscato focuses on topics pertaining to Problem domain under Local search, and may sometimes address concerns connected to Heuristics.
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On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts : Towards Memetic Algorithms
Caltech Concurrent Computation Program (1989)
New Ideas In Optimization
David Corne;Marco Dorigo;Fred Glover;Dipankar Dasgupta.
Memetic algorithms: a short introduction
New ideas in optimization (1999)
A Gentle Introduction to Memetic Algorithms
Pablo Moscato;Carlos Cotta.
Handbook of Metaheuristics (2003)
Genome-wide analysis of long noncoding RNA stability
Michael B. Clark;Rebecca L. Johnston;Mario Inostroza-Ponta;Archa H. Fox.
Genome Research (2012)
Genome-wide association study identifies new multiple sclerosis susceptibility loci on chromosomes 12 and 20
Melanie Bahlo;David R Booth;Simon A Broadley;Matthew A Brown;Matthew A Brown.
Nature Genetics (2009)
On the Rank of Extreme Matrices in Semidefinite Programs and the Multiplicity of Optimal Eigenvalues
Pablo Moscato;Michael G. Norman;Gabor Pataki.
Mathematics of Operations Research (1998)
Handbook of Memetic Algorithms
Ferrante Neri;Carlos Cotta;Pablo Moscato.
Handbook of Memetic Algorithms (2011)
Genome-wide meta-analysis identifies novel multiple sclerosis susceptibility loci
Nikolaos A. Patsopoulos;Federica Esposito;Joachim Reischl;Stephan Lehr.
Annals of Neurology (2011)
A memetic algorithm for the total tardiness single machine scheduling problem
Paulo M França;Alexandre Mendes;Pablo Moscato.
European Journal of Operational Research (2001)
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