Sentence examples for machine learning predictions from inspiring English sources

MoRFpred predicts sequences that undergo disorder-to-order transitions of all types of MoRFs (α, β, coil, and complex) using a combination of sequence alignment and machine learning predictions based on amino acid properties, predicted disorder, B-factors, and solvent accessibility.

a matrix of predictions of gene-disease relationships based on known relationships mined from the literature and machine learning predictions [29].

It appears that the data differences between the survey training set and the DLS-100 set had very little effect on the quality of the machine learning predictions and therefore are unlikely to have had a substantial effect on the human predictions.

In this special issue, we take an interest from mathematicians, bioinformaticians, computational scientists, and engineers together with experimental immunologists, to present and discuss latest developments in different subareas ranging from modeling and simulation to machine learning predictions and their application to basic and clinical immunology.

This ranking can be used to build a predictive model, without eliminating any variables, using any other machine learning prediction method, in this case and differently from Genuer et al [43], Support Vector Machines [48], inserting the variables stepwise in order to find a good balance between the number of variables and prediction error.

Therefore, in order to use the machine learning prediction algorithms sliding window technique is used.

The key to understanding AnalyticsMD's proposition is the large-scale, machine learning prediction engine at the core of their platform, say co-founders Brent Newhouse and Mudit Garg.

The ongoing expansion of public [40, 46 49] and proprietary [50] target-ligand binding data begins to enable "machine learning" prediction of target inhibition profiles [51 56].

Typically, two strategies can be employed to perform standard comparison between distinct machine learning prediction models for binary classification problems, either through cross-validation experiments or test on the independent datasets given the same threshold value.

For machine learning prediction of stability changes, each mutation needs to be encoded with a number of predictive features.

However, most supervised machine learning prediction models treat the drug target interaction prediction as a binary classification problem (i.e. interaction or no interaction).