The two are inseparable--good science attracts clients and collaborators along with more challenging samples, which themselves lead to more interesting results, and so on.
In turn, this permits molecular binding to be screening in more challenging natural samples or at very low concentration where running multiple experiments across a range of mixing times (i.e. full NOE build-up curves) would be time prohibitive.
They therefore do not require parallel spectral calibration measurements and are independent of the optical system, which makes them attractive for more challenging biological samples such as live animal models, for which the spectral properties can change in space and time.
The complexity of the human urinary proteome, the extensive post-translational processing of its proteins, the interindividual and intraindividual variability of protein content based on diet, exercise, sexual activity, and microbial colonization also render biomarker discovery projects more challenging than other sample sources.
The pursuit of evidence stronger than the genome-wide significance is thus more challenging, and larger sample sizes in non-European studies and meta-analyses of ethnically mixed populations are required to compensate for variations in LD patterns from European populations.
It should be pointed out that, complicated interpretation of WGS data are even more challenging in tumor samples confounded by both tumor impurity and aneuploidy, as they usually cannot be solved separately (Oesper et al., 2013).
On the other hand, transcriptome assembly for multiple samples and subsequent differential analysis are more challenging because (i) multiple sample RNA-Seq data typically contains more noise and (ii) differential analysis is very sensitive to assembly and abundance estimation errors.
However, microarray dataset suffers from the curse of dimensionality, the limited number of samples, and the irrelevant and noise genes, all of which make the classification task for a given sample more challenging [ 1, 5, 6].
The planner has been tested in two domains: an exploration mission consisting of pictures acquisition, and a more challenging one that includes samples delivering.
Our results show that i) model selection and parameter estimation improve with larger sample sizes and ii) consistent with previous work [ 19, 20], recent demographic events are more challenging and require substantially larger sample sizes for accurate inference.
