Name
Abstract | ||
|---|---|---|
We have developed an efficient general-purpose segmentation tool and showed that it had comparable or more accurate results than many of the most popular segment-calling algorithms used in contemporary genomic data analysis. iSeg is capable of analyzing datasets that have both positive and negative values. Tunable parameters allow users to readily adjust the statistical stringency to best match the biological nature of individual datasets, including widely or sparsely mapped genomic datasets or those with non-normal distributions. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1186/s12859-018-2140-3 | BMC Bioinformatics |
Field | DocType | Volume |
Genome,Data mining,Biology,Genomics,Dynamic programming,Data structure,Epigenomics,Segmentation,Binary tree,Algorithm,Statistical model,Bioinformatics,Genetics | Journal | 19 |
Issue | ISSN | Citations |
1 | 1471-2105 | 1 |
PageRank | References | Authors |
0.35 | 18 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Senthil B. Girimurugan | 1 | 1 | 0.69 |
| Yuhang Liu | 2 | 2 | 1.17 |
| Pei-Yau Lung | 3 | 1 | 0.35 |
| Daniel L. Vera | 4 | 1 | 1.03 |
| Jonathan Dennis | 5 | 12 | 3.51 |
| Hank W. Bass | 6 | 12 | 1.01 |
| Jinfeng Zhang | 7 | 86 | 10.11 |