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It is a challenge to infer the best sparse polynomial chaos expansion of a given model input/output data set.
Studies on drug repositioning have recently been rigorously carried out, and it is a difficult challenge to infer novel drug indications from a large amount of multi-level biomedical interaction networks.
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The next challenge to inferring yearly growth increments in this dataset is that census intervals are uneven.
The detailed archival records provided the opportunity to examine behavioral differences that are challenging to infer from daily histograms.
Moreover, it is challenging to infer the pharmacological effect of a component using in vitro experiments alone.
Given these differences, it is challenging to infer what was the ancestral situation, i.e. what were the main proneural genes acting in Urbilateria, the last common ancestor of all bilaterians.
It is challenging to infer the precise impact of school closures in Hong Kong, given that they were implemented immediately and sustained until summer vacation and so we have little data on local transmissibility in the absence of school closures.
The initial challenges to inferring evolutionary relationships among the ditrysian superfamilies have been attributed to their morphological homogeneity, and thus to the difficulty of finding distinct, unique morphological characters uniting superfamilies and supporting descent from a common ancestor [ 2, 13].
The challenge is to infer reliable knowledge from big and noisy data.
The next challenge is to infer, from the resulting DNA DNA contact maps, accurate 3D models of how chromosomes fold and fit into the nucleus.
One exercise, for example, challenges students to infer the behaviors of two animals based on a pattern of fossil footprints.
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