The uncertainty evolving around micro- and macro-level determinants influencing antimicrobial resistance makes long-term prediction challenging.
While a number of methods have been reported to predict protein function from protein sequence [ 1– 3], protein structure [ 4, 5], protein-protein interaction network [ 6, 7], and evolutionary relationships [ 8– 10], the complexity of protein function makes function prediction challenging.
We have added discussion of this point in the subsection "Multidimensional chemical mapping allows rapid modeling of diverse non-coding RNAs, including blind prediction challenges", as quoted in our response to an earlier point.
Subsection "Multidimensional chemical mapping allows rapid modeling of diverse non-coding RNAs, including blind prediction challenges": "The lack of correlation between the fraction of satisfied MOHCA-seq pairwise constraints and the accuracy of the models (Table 2) as has been observed in prior macromolecule modeling based on pairwise information.
Using wind energy effectively for electrical power generation requires good predictions of wind speed, and the intermittent nature of wind makes such predictions challenging.
This limits this method to only small sample sizes, making statistical analysis and predictions challenging.
Knowledge about the identity and positioning of these enhancer patterns is lacking, making correct peroxisome targeting predictions challenging.
Consequently, the quality of models submitted to the blind prediction challenge CAPRI (Critical Assessment of PRedicted Interactions) has steadily increased, including complexes predicted from homology structures of one binding partner and complexes with atomic accuracy at the interface.
The DIGRE model won the best performance in the National Cancer Institute's "DREAM 7 Drug Combination Synergy Prediction Challenge," an international crowdsourcing-based computational challenge for predicting drug combination effects using transcriptome data.
However predicting what the user wants becomes very difficult.There are various prediction challenges which are faced, some of them includes long training time, more prediction time, low prediction accuracy, memory limitation etc.
