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Artificial intelligence and neural network based methods require difficult to obtain training sets.

Indirect culture based methods require handling of large numbers of bacilli and stringent safety precautions are necessary, including negative pressure rooms and microbiological safety cabinets.

The presented physical model predicts secondary structure at least as well as advanced statistical based methods requiring known template structures.

Although TERA, like other parsimony based methods, requires the prior specification of the costs associated with duplication, transfer and loss (DTL) events, it does not require prior assumptions about a statistical support threshold, as it estimates a self-consistent support threshold from its input.

To localize unknown targets, most template-based methods require a large set of well-engineered templates.

However, prognostic models, particularly probabilistic-based methods, require a large number of failure instances.

These fuzzy relation-based methods require a decision maker to perform pair-wise comparisons for the similarity among criteria as forming a fuzzy relation matrix.

However, in case of the ModuLand method family this adaptation is achieved by the choice of the sub-steps (like the community landscape construction method), while the Q modularity-based methods require a null-model to be chosen, which is reflecting the experimenter's expectations about the network.

Anchor-based methods require a separate subjective assessment measure of a patient's perceived benefit from an intervention, data that were not collected by our institution's joint replacement registry [ 43].

The Monte Carlo simulation based method requires considerable computational efforts for the estimation of functional reliability analysis.

In contrast to docking methods, ligand‐based VS methods require a biologically active query molecule.

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