Enzyme stability is an important parameter in biocatalytic applications, and there is a strong need for efficient methods to generate robust enzymes.
In our approach the developed method for combined transcriptome and genome sequencing generated robust and quantitative data for studying gene expression, isoforms etc. on one hand and the genome on the other hand.
Advances in PCR based genomic approaches have generated robust DNA marker systems [ 3, 4], which offer an effective approach to augment breeding methods for mulberry improvement [ 5].
Phylogenetic trees were constructed by various methods generating a robust phylogeny of the genus Pristionchus.
This method generated a robust DNA sequence alignment of both distantly and closely related species, as direct DNA sequence alignment may produce unreliable alignments by introducing many indels (gaps).
RMA and PLIER are both model-based methods, which aim to generate robust signal estimates by down-weighing poorly performing probes.
In addition, practical means for implementing the method are derived including generating robust joint constellations, decoding data with low complexity, and mitigating the effects of imperfections due to mobility, power control errors, and synchronization errors.
However, it has been observed that the aggregation of different network inference methods generates high-quality robust results (Marbach et al., 2012).
The seminal method of Lee and Carter (1992) generates robust mortality forecasts using a parsimonious principal component model that captures age and period effects.
However, as with any method, sound experimental design is essential to generate robust results from microarray experiments, particularly given the issues of high dimensionality [ 3].
