Fabrizio Costa
TitleCited byYear
An updated evolutionary classification of CRISPR–Cas systems
KS Makarova, YI Wolf, OS Alkhnbashi, F Costa, SA Shah, SJ Saunders, ...
Nature Reviews Microbiology 13 (11), 722, 2015
Fast neighborhood subgraph pairwise distance kernel
F Costa, K De Grave
Proceedings of the 26th International Conference on Machine Learning, 255-262, 2010
GraphProt: modeling binding preferences of RNA-binding proteins
D Maticzka, SJ Lange, F Costa, R Backofen
Genome biology 15 (1), R17, 2014
Weighted decomposition kernels
S Menchetti, F Costa, P Frasconi
Proceedings of the 22nd international conference on Machine learning, 585-592, 2005
Towards incremental parsing of natural language using recursive neural networks
F Costa, P Frasconi, V Lombardo, G Soda
Applied Intelligence 19 (1-2), 9-25, 2003
GraphClust: alignment-free structural clustering of local RNA secondary structures
S Heyne, F Costa, D Rose, R Backofen
Bioinformatics 28 (12), i224-i232, 2012
Random forest based feature induction
C Vens, F Costa
2011 IEEE 11th International Conference on Data Mining, 744-753, 2011
Learning first-pass structural attachment preferences with dynamic grammars and recursive neural networks
P Sturt, F Costa, V Lombardo, P Frasconi
Cognition 88 (2), 133-169, 2003
Lineage-specific splicing of a brain-enriched alternative exon promotes glioblastoma progression
R Ferrarese, GR Harsh, AK Yadav, E Bug, D Maticzka, W Reichardt, ...
The Journal of clinical investigation 124 (7), 2861-2876, 2014
Classification of small molecules by two-and three-dimensional decomposition kernels
A Ceroni, F Costa, P Frasconi
Bioinformatics 23 (16), 2038-2045, 2007
klog: A language for logical and relational learning with kernels
P Frasconi, F Costa, L De Raedt, K De Grave
Artificial Intelligence 217, 117-143, 2014
CRISPRstrand: predicting repeat orientations to determine the crRNA-encoding strand at CRISPR loci
OS Alkhnbashi, F Costa, SA Shah, RA Garrett, SJ Saunders, R Backofen
Bioinformatics 30 (17), i489-i496, 2014
Effective feature construction by maximum common subgraph sampling
L Schietgat, F Costa, J Ramon, L De Raedt
Machine Learning 83 (2), 137-161, 2011
Characterizing leader sequences of CRISPR loci
OS Alkhnbashi, SA Shah, RA Garrett, SJ Saunders, F Costa, R Backofen
Bioinformatics 32 (17), i576-i585, 2016
Semi-supervised prediction of SH2-peptide interactions from imbalanced high-throughput data
K Kundu, F Costa, M Huber, M Reth, R Backofen
PloS one 8 (5), e62732, 2013
MoDPepInt: an interactive web server for prediction of modular domain–peptide interactions
K Kundu, M Mann, F Costa, R Backofen
Bioinformatics 30 (18), 2668-2669, 2014
Wide coverage natural language processing using kernel methods and neural networks for structured data
S Menchetti, F Costa, P Frasconi, M Pontil
Pattern Recognition Letters 26 (12), 1896-1906, 2005
AptaTRACE elucidates RNA sequence-structure motifs from selection trends in HT-SELEX experiments
P Dao, J Hoinka, M Takahashi, J Zhou, M Ho, Y Wang, F Costa, JJ Rossi, ...
Cell systems 3 (1), 62-70, 2016
Wide coverage incremental parsing by learning attachment preferences
F Costa, V Lombardo, P Frasconi, G Soda
Congress of the Italian Association for Artificial Intelligence, 297-307, 2001
BlockClust: efficient clustering and classification of non-coding RNAs from short read RNA-seq profiles
P Videm, D Rose, F Costa, R Backofen
Bioinformatics 30 (12), i274-i282, 2014
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