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In addition, we implement PPI predictions to interpret biological data.
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In his keynote talk, Dr. Pan showed how to use machine learning methods to improve the accuracy of protein structure prediction and to interpret prediction results.
The results showed that the new procedure could effectively deduce protein folds without high-resolution structural data, a feature that could also be used to recognize native fold in structure prediction and to interpret data in fields like structure genomics.
The sequences indicate recent movement of RVFV and support the need for surveillance to recognize when and where RVFV circulates between epidemics, which can make data from prediction tools easier to interpret and preventive measures easier to direct toward high-risk areas.
As a consequence, it depends on the interpretation of the theoretical predictions as to how to interpret Stockley et al.'s results.
Model predictions were also used to interpret previously published data on the delivery of the steroid dexamethasone from an ethylene vinyl acetate copolymer implant in rat brain.
With a comprehensible predictor that is easily communicated to the user, predictions can be easier to interpret and verify.
The quality of predictions and the ability to interpret them biologically also depends on the quality of the data.
However, existing computational approaches have the following disadvantages: (1) the prediction results are hard to interpret; and (2) they ignore multi-location bacterial proteins.
In addition, it includes 'users' of such studies who wish to understand the basic premise, design, and limitations of genetic prediction studies in order to interpret the results for their potential application in health care.
In addition, it includes "users" of such studies who wish to understand the basic premise, design, and limitations of genetic prediction studies in order to interpret the results for their potential application in healthcare.
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