The most important contribution of the present unsupervised, alignment-free method should be to predict functions of increasingly vast quantity of function-unknown proteins derived from less characterized genomes, such as those studied in the metagenomic approaches.
One important contribution of the present alignment-free clustering method is to systematically and efficiently predict functions of the increasingly vast quantity of function-unknown proteins derived from poorly characterized microbe genomes.
The sequence homology searches, such as BLAST and PSI-BLAST, undoubtedly are essential tools for predicting protein function, but there has been accumulated a vast quantity of function-unknown proteins.
Passive intervertebral motion (PIVM) assessment is used to judge the quantity and quality of functions of spinal motion segments and is assumed to play an important role in diagnostically classifying patients and selecting treatment [ 18].
This approach should add a new and powerful tool to the genomics and proteomics fields for the systematical and efficient prediction of functions of huge quantities of function-unknown proteins progressively accumulated in the International DNA Banks.
Such a formula is composed of a certain quantity of optimized functions and weights.
The geometric condition that our underlying domain should be hyperconvex is to ensure that we have a satisfying quantity of plurisubharmonic functions.
In other words, the 1SZO NWs easily become Ag-doping in the ZnO-based structure because of both low E a and optimized quantity of substitution function of Ag+ [19].
The advantage of generalized-ensemble simulations lies in the fact that they not only avoid getting trapped in states of energy local minima but also allow the calculations of physical quantities as functions of temperature or other parameters from a single simulation run.
In this work, we only consider three diverse scoring functions AMBER, X-Score and KScore to demonstrate the efficacy of the multi-objective strategy, but the strategy is not restricted to these scoring function and even not to the quantity of the scoring functions.
Additionally, we linearly approximate only the computationally intensive part of inference -- the optimization of the global parameters -- and retain the nonlinearity of easily computed quantities as functions of the global parameters.
