| Reference : Ties between graph theory and biology |
| Parts of books : Contribution to collective works | |||
| Engineering, computing & technology : Computer science | |||
| http://hdl.handle.net/10993/14830 | |||
| Ties between graph theory and biology | |
| English | |
| Blazewicz, Jacek [] | |
| Kasprzak, Marta [] | |
Vlassis, Nikos  [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >] | |
| 2013 | |
| 2nd | |
| Handbook of Graph Theory | |
| Gross, Jonathan L. | |
| Yellen, Jay | |
| Zhang, Ping | |
| Chapman and Hall/CRC | |
| Series:Discrete Mathematics and Its Applications | |
| Yes | |
| 9781439880180 | |
| [en] Last decades brought us a new scientific area of computational biology, placed at the junction of biology (especially molecular biology), computer science and mathematics. Its aim is to solve real-world problems arising in biology with the use of mathematical models and methods, and tools from computer science. Molecular biology, due to its rapid progress, yields more and more experimental data, possible to be processed on computers only. Efficient processing and advisable analysis must be accompanied by well suited models and methods, the ones coming from graph theory frequently appeared to be most useful. Here, the most interesting and breakthrough approaches of computational biology tied with graph theory are characterized. | |
| Luxembourg Centre for Systems Biomedicine (LCSB): Machine Learning (Vlassis Group) | |
| http://hdl.handle.net/10993/14830 |
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