ABiNet AbiNet

ABiNet (Asymmetric alignment of Biological Networks) is a software tool designed to align and query biological networks from a special point of view, that is, a kind of asymmetric alignment. In particular, given two input networks, that associated to the best characterized organism (called Master) is exploited as a fingerprint to guide the alignment process to the second input network (called Slave), so that generated results preferably retain the structural characteristics of the Master network. Technically, this is obtained by generating from the Master a finite automaton, called alignment model, which is then fed with a (linearization of) the Slave for the purpose of extracting, via the Viterbi algorithm, matching subgraphs.

Related Publications:

  • Nicola Ferraro, Luigi Palopoli, Simona Panni and Simona E. Rombo. Asymmetric Comparison and Querying of Biological Networks. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 8(4), 876-889, ISSN 1545-5963, IEEE Computer Society, Los Alamitos, CA (USA), 2011.
  • Nicola Ferraro, Luigi Palopoli, Simona Panni and Simona E. Rombo. "Master-Slave" Biological Network Alignment. In Proceedings of 6th International symposium on Bioinformatics Research and Applications (ISBRA 2010), pp. 215-229, LNBI/LNCS 6053 Springer 2010, Connecticut, USA, May 23-26, 2010.
  • Nicola Ferraro, Luigi Palopoli, Simona Panni and Simona E. Rombo. Asymmetric Global Alignment of Protein-Protein Interaction Graph Databases (Extended Abstract). In Atti del Convegno Nazionale su Sistemi Evoluti per Basi di Dati (SEBD 2010), ISBN 978-88-7488-369-1, Rimini, Italy, June 20-23, 2010.



RanCoC Taxonomies

RanCoC (Random protein interaction network Co-Clustering) is a method based on a co-clustering approach to search for functional modules in protein-protein interaction networks. The input protein-protein interaction network is represented by the corresponding binary adjacency matrix, where rows and columns correspond to proteins and 1 entries correspond to interactions. RanCoC applies a greedy search technique for finding local optimal solutions made of dense sub-matrices containing the maximum number of ones.

Related Publications:

  • Clara Pizzuti and Simona E. Rombo. A co-clustering approach for mining large protein-protein interaction networks. IEEE/ACM Transactions on Computational Biology and Bioinformatics. ISSN 1545-5963, IEEE Computer Society, Los Alamitos, CA (USA), 2012 (To Appear).
  • Clara Pizzuti and Simona E. Rombo. Multi-functional Protein Clustering in PPI Networks. In Proceedings of the 2nd International Conference on Bioinformatics Research and Development (BIRD 2008), pp. 318-330, Communications in Computer and Information Science 13 Springer 2008, Vienna, Austria, July 7-9, 2008.
  • Clara Pizzuti and Simona E. Rombo. Discovering meaningful protein-protein interaction modules by a co-clustering based approach. In Atti del Convegno Nazionale su Sistemi Evoluti per Basi di Dati (SEBD 2008), pp. 294-301, Palermo, Italy, June 22-24, 2008.
  • Clara Pizzuti and Simona E. Rombo. PINCoC: a Co-Clustering based Method to Analyze Protein-Protein Interaction Networks. In Proceedings of the 8th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2007), pp. 821-830, LNCS 4881 Springer 2007, Birmingham, UK, December 16-19, 2007.



2D IME 2DIME

2D IME (2D Irredundant Motif Extraction) is a C++ open-source library that provides many utilities for the extraction of motifs from 2D digital images. In particular, compact trie and other suitable data structures and functions are provided to manage strings and arrays, and an efficient implementation of an incremental algorithm to extract basis of 2D motifs is included in the library. This project is in collaboration with the College of Computing of the Georgia Institute of Technology.

Related Publications:

  • Alessia Amelio, Alberto Apostolico and Simona E. Rombo. Image Compression by 2D Motif Basis. In Proceedings of IEEE Data Compression Conference (DCC 2011), IEEE CS Press, Snowbird, UT, USA.
  • Simona E. Rombo. Optimal extraction of motif patterns in 2D. Information Processing Letters, 109(17): 1015-1020, 2009.
  • Alberto Apostolico, Laxmi Parida and Simona E. Rombo. Motif Patterns in 2D. Theoretical Computer Science, 390(1): 40-55, 2008.



BiGraPPIN BiGraPPIN

BiGraPPIN (Bipartite Graph based Protein-Protein Interaction Network alignment) is an algorithm to search for similarities across PPI networks. The technique core consists in computing a maximum weight matching of bipartite graphs to compare the neighborhoods of pairs of proteins in different PPI networks. The main idea is that proteins belonging to different networks should be matched look- ing not only at their own sequence similarity, but also at the similarity of proteins they "strongly" interact with, either directly or indirectly.

Related Publications:

  • Valeria Fionda, Simona Panni, Luigi Palopoli and Simona E. Rombo. A technique to search functional similarities in PPI networks. International Journal of Data Mining and Bioinformatics, 3(4): 431-453, ISSN 1748--5673, 2009.
  • Valeria Fionda, Simona Panni, Luigi Palopoli and Simona E. Rombo. Bi-GRAPPIN: Bipartite graph based protein-protein interaction networks similarity search. In Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2007), pp. 355-361, IEEE CS Press, Silicon Valley, USA, November 2-4, 2007.



Biological Database Classification Taxonomies

An accurate analysis and classification of biological data and repositories is necessary to facilitate the access and recovery of such data. We propose a taxonomic classification of biological databases, considering both a computer science and a biological point of view. We started referring to the databases published on [Galperin 2006], analyzed all the almost 800 available biological databases and classified them according to their contents and features. Then, we focused on the explosion of a subset of the previously classified databases, that are, genomic databases, and we provide a more detailed classification of almost 80 repositories.

Related Publications:

  • Erika De Francesco, Giuliana Di Santo, Luigi Palopoli and Simona E. Rombo. A summary of genomic databases: overview and discussion. In Biomedical Data and Applications (Amandeep S. Sidhu, Tharam S. Dillon and Elizabeth Chang Eds.), Ch. III, pp. 37-54, Springer, ISBN 978-3-642-02192-3, Heidelberg (Germany), 2009.



JSSPrediction JSSPrediction

JSSPrediction is method for protein secondary structure prediction based on integrating the prediction results computed by some available on-line prediction tools to obtain a combined prediction of higher quality. Given an input protein p whose secondary structure has to be predicted, and a group of proteins F, whose secondary structures are known, the server currently works by first selecting a set of predictors good at predicting the secondary structure of proteins in F and then it integrates the prediction results delivered for p by the selected team of prediction tools.

This project was in collaboration with the Universitá degli Studi "Magna Graecia" of Catanzaro.

Related Publications:

  • Luigi Palopoli, Simona E. Rombo, Giorgio Terracina, Giuseppe Tradigo and Pierangelo Veltri. Improving protein secondary structure predictions by prediction fusion. Information Fusion, 10(3): 217-232, 2009.
  • Luigi Palopoli, Simona E. Rombo, Giorgio Terracina, Giuseppe Tradigo and Pierangelo Veltri. JSSPrediction: a Framework to Predict Protein Secondary Structures Using Integration. In Proceedings of the 19-th IEEE Symposium on Computer-Based Medical Systems (CBMS 2006), pp. 931-935, IEEE CS Press, Salt Lake City, Utah, June 22-23, 2006.
  • Luigi Palopoli, Simona E. Rombo, Giorgio Terracina, Giuseppe Tradigo and Pierangelo Veltri. Protein Secondary Structure Prediction: How to Improve Accuracy by Integration. Third International Meeting of Bioinformatics and Biostatistic (CIBB 2006), in Proceedings of the 7-th Int. FLINS Conf., pp. 579--586, World Scientific Printers, Genova, Italy, August 29-31, 2006.