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Especially with the growing availability of large and highly-resolved datasets it will be more important than ever that issues of data quality are taken seriously.
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For the NASA GISTEMP dataset, it will be 1.5°F above the long-term average.
As the human dataset grows, it will be of great interest to examine further the extent of interactions between ethanol and PI3K-dependent growth factors.
In addition, some of the algal gene information in AlgaePath is limited; therefore, as more high-throughput experimental datasets become available it will be essential to continuously update the high-throughput data in AlgaePath.
Once a comprehensive quantitative proteomics dataset is available, it will be important to validate that the same functional groups are under-predicted.
However, this may not be true for the other datasets and in that case, it will be better to use the medoid-based approach instead of the mean-based one.
In sequence datasets with clear phylogenetic structure, it will be essential to compute conditional Ka/Ks for specific branches of the phylogenetic tree, as has been done for standard Ka/Ks calculations [ 33, 46].
Most of these companies sell their dataset to larger organizations and it will be interesting to see how decentralized projects distribute theirs in the future.
As more multidimensional datasets become available in the future, it will be interesting to test whether such pathway inferences provide robust biomarkers that generalize across cohorts.
If large population datasets for cancer screening become available, it will be interesting for future studies to examine the possible interaction effects of sensitivity and risk difference, systematic error and risk difference, or a possible three-way interaction of sensitivity-systematic error-risk difference.
In the next section, we describe how our dataset has been created and how it will be used in the experiments.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com