Name
Abstract | ||
|---|---|---|
Background: Over the last years, more and more biological data became available. Besides the pure amount of new data, also its dimensionality - the number of different attributes per data point - increased. Recently, especially the amount of data on chromatin and its modifications increased considerably. In the field of epigenetics, appropriate visualization tools designed for highlighting the different aspects of epigenetic data are currently not available. Results: We present a tool called TiBi-Scatter enabling correlation analysis in 2D. This approach allows for analyzing multidimensional data while keeping the use of resources such as memory small. Thus, it is in particular applicable to large data sets. Conclusions: TiBi-Scatter is a resource-friendly and easy to use tool that allows for the hypothesis-free analysis of large multidimensional biological data sets. |
Year | Venue | Field |
|---|---|---|
2014 | CoRR | Data mining,Biological data,Data set,Visualization,Matrix (mathematics),Computer science,Curse of dimensionality,Artificial intelligence,Chromatin,Machine learning,Correlation analysis |
DocType | Volume | Citations |
Journal | abs/1407.2084 | 1 |
PageRank | References | Authors |
0.35 | 8 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dirk Zeckzer | 1 | 1 | 2.04 |
| Daniel Gerighausen | 2 | 3 | 1.05 |
| Lydia Steiner | 3 | 1 | 0.35 |
| Sonja J. Prohaska | 4 | 145 | 11.56 |