Name
Abstract | ||
|---|---|---|
A number of techniques have been developed in order to address issues such as genome, trascriptome and proteome analysis. However, a time and cost effective technique for interactome analysis is still lacking. Lots of methods for the predicion of protein-protein interacions have been developed: some of them are based on high quality alignment of sequences, others are based on the tridimensional features of proteins, but they all bear strong limitations that make impossible their large scale application. Recently, an SVM-based machine learning approach has been used to address this topic. Although the method was able to correctly classify 80% of the test samples, it was not applied to the prediction of yet unknown interactions. In this work, we address this topic and show that an optimized, SVM-based machine learning approach trained with combinations of shuffled sequences as examples of lack of interaction is unable to make large scale predictions of interaction. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1007/978-3-540-45216-4_33 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
machine learning,cost effectiveness | Interactome,Protein–protein interaction,Computer science,Support vector machine,Proteome,Artificial intelligence,Machine learning | Conference |
Volume | ISSN | Citations |
2859 | 0302-9743 | 2 |
PageRank | References | Authors |
0.42 | 3 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Francesco Marangoni | 1 | 2 | 0.42 |
| Matteo Barberis | 2 | 31 | 4.05 |
| Marco Botta | 3 | 284 | 41.98 |