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For the NASA GISTEMP dataset, it will be 1.5°F above the long-term average.
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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.
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.
Once a comprehensive quantitative proteomics dataset is available, it will be important to validate that the same functional groups are under-predicted.
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.
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.
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].
In the next section, we describe how our dataset has been created and how it will be used in the experiments.
Since the patent corpus continues to grow quickly we can envisage that if the workflow and data processing pipeline is applied on an ongoing basis then the dataset will continue to grow and it will be at a much faster rate than manual extraction and curation will allow.
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.
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