Name
Abstract | ||
|---|---|---|
Taken together, our results highlight the importance of using dedicated methods for the analysis of Hi-C cancer data. Both CAIC and LOIC methods perform well on simulated and real Hi-C data sets, each fulfilling different needs. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1186/s12859-018-2256-5 | BMC Bioinformatics |
Keywords | Field | DocType |
Cancer,Copy-number,Hi-C,Normalization | Genome,Simple extension,Ploidy,Chromosome,Normalization (statistics),Copy-number variation,Biology,Matrix (mathematics),Algorithm,Bioinformatics,Genetics | Journal |
Volume | Issue | ISSN |
19 | 1 | 1471-2105 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nicolas Servant | 1 | 129 | 8.53 |
| Nelle Varoquaux | 2 | 2 | 1.03 |
| Edith Heard | 3 | 10 | 1.27 |
| Emmanuel Barillot | 4 | 950 | 165.00 |
| Jean-philippe Vert | 5 | 2754 | 158.52 |