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While diversified data sets have led to numerous opportunities and studies for discovering new disease marker candidates, the success of those efforts has been largely disappointing in terms of validating the results across populations [ 4].
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The danger is increasing also thanks to the emerging capability to integrate diversified data.
One is to diversify your data set and to have many different annotations of your data set, like race and gender and age.
Future improvements will come in the form of improved gene annotations, a greater sequencing depth in the genus for picking up weaker signals of diversifying selection as well as the generation of in planta expression data sets for other Fusarium species that capture early time points during infection reliably for effector prediction.
Two likelihood-ratio tests (M1a against M2a and M7 against M8) indicated that sites were under diversifying selection for the CC-NB (M7/M8, χ2 = 145, P < 0.001) and LRR data sets (M7/M8, χ2 = 246, P < 0.001).
As more CRM1-binding nuclear export signals are discovered in the future, the experimental data sets used to train the computational programs that are currently used to locate these signals in genomic sequences will be diversified and improved.
Many data sets show this.
Code and data sets.
Data sets upon data sets.
for different data sets.
Our analysis uses two large data sets.
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