The phrase "a less sparse" is correct and usable in written English.
It can be used when comparing the density or fullness of something, indicating that it is not as sparse as another reference point.
Example: "The new design features a less sparse layout, making it more visually appealing to users."
Alternatives: "a denser" or "a fuller".
Using these methods, users will either get a higher resolution (in terms of genomic coordinates) in the resulting dataset (union, pairwise) or a less sparse expression matrix (greedy, minmax).
Thus, if a less sparse solution yields a higher generalization performance (as estimated by cross-validation) than a more sparse solution, then the less sparse solution is selected.
However, a large number of MKL methods have been proposed (Gönen and Alpaydin, 2011) and some methods with a less sparse solution may give higher accuracy (Kloft et al., 2011).
Both autoregulatory circuits lead to a target distribution less sparse than a sTF, showing that they effectively reduce fluctuations, but the iMSL distribution is clearly more peaked than the tSL one.
We recognise that a larger and less sparse data set would be desirable in order to assess better the TBC performance on this particular problem.
The motivation for using deduplicated sequence read counts for scoring overlap with feature modifications is that it results in a richer (i.e., less sparse) matrix than scoring binary overlap with stable nucleosomes for the feature modifications.
Prior to clustering, the network used for link prediction was made less sparse by applying an iterative degree cutoff (≥3).
When considering the average proportion of single CNVs per CNVRs (CNV density) within each breed, it can be observed that the Italian Brown has a more concentrated distribution (more CNVs per CNVRs), two times less sparse than the Italian Simmental, the Piedmontese and the Marchigiana (p-value < 0.006).
This article presents a learning framework of feature transformation, addressing the sparseness problem by transforming sparse conventional base features into less sparse high-level features (i.e. meta features) with the help of a large amount of automatically annotated data.
A larger value of λ leads to a sparse regression that fits the data less well and a smaller λ leads to regression that fits the data well but is less sparse.
Thus, DAM-DALEC can provide independent high-confidence in vivo data, which even for a fraction of the genome can be used to refine, validate, or evaluate less sparse but potentially more 'invasive' nuclease-based assays.
