Predictive ability, evaluation

Lawrence NL, Cox SE, Brady HJ (1977) Treatment of melasma with Jessner s solution versus glycolic acid a comparison of clinical efficacy and evaluation of the predictive ability of Wood s light examination. J Am Acad Dermatol 36 589-593... 

As mentioned in Section 5.7.1, it is recommended to use an appropriate resampling procedure for the evaluation. A separate classifier is developed from each training set, and its performance is evaluated from the corresponding test set. This will result in several values for the predictive abilities, and statistical measures can be used to describe their distributions. 

The suitability and general applicability of an artificial membrane and PAMPA in vitro permeation methods were evaluated for their ability to predict drug absorption potential in comparison to Caco-2 cell literature data [57], A linear correlation (R2 = 0.957) was obtained between artificial membrane Papp and human absorption data, indicating the good predictive ability of the proposed method for HP compounds with greater differentiation of drugs with /a below 50% [57],... 

To further evaluate the ability of our 3D-logP description to model the HIV dataset, we used different ways to design training sets and test sets. By doing so, we expected to have a better idea of the real predictive ability of the 3D-logP-based PLS model. For the first design, we reproduced the split training/test sets published by Holloway and for the second one we applied a... 

To test the models, the training set is divided into calibration and validation sets, as shown in Table 4.26. The predictive ability of the TE and MEK SIMCA models is then evaluated using samples from all 10 classes. 

They are an optimistic estimate of prediction ability (they only reflect precision) and should be evaluated relative to the requirements of the application. 

Dias and coworkers utilized an array of potentiometric sensors for the classification of honey samples from different Portuguese regions with respect to the predominant pollen type Erica, Echium, Lavandula. PCA and LDA were employed for the pattern recognition (see Fig. 2.25), after having verified that the variables followed a normal distribution. Cross-validation was applied for evaluating the classification rules, obtaining satisfactory prediction abilities for two classes (about 80%) and poor results for the third one (about 50%) (Dias et al., 2008). 

Perform a correct and extensive validation of models, in order to properly evaluate their prediction ability and thus their actual applicability. In particular, mind that if model building involves optimization steps, a three-set validation strategy should be applied. 

Two methods are used to evaluate the predictive ability for LDA and for all other classification techniques. One method consists of dividing the objects of the whole data set into two subsets, the training and the prediction or evaluation set. The objects of the training set are used to obtain the covariance matrix and the discriminant scores. Then, the objects of the training set are classified, so obtaining the apparent error rate and the classification ability, and the objects of the evaluation set are classified to obtain the actual error rate and the predictive ability. The subdivision into the training and prediction sets can be randomly repeated many times, and with different percentages of the objects in the two sets, to obtain a better estimate of the predictive ability. 

Elastic Properties. The ability of a fiber to deform under below-rupture loads and to return to its original configuration or dimension upon load removal is an important performance criterion. Permanent deformation may be as detrimental as actual breakage, rendering a product inadequate for further use. Thus, the repeated stress or strain characteristics are of significance in predicting or evaluating functional properties. 

We can summarise some other ideas for evaluating the predictive ability of the PLS model. First, you can compare the average error (RMSEP) with the concentration levels of the standards (in calibration) and evaluate whether you (or your client) can accept the magnitude of this error (fit-for-purpose). Then, it is interesting to calculate the so-called "ratio of prediction to deviation", which is just RPD=SD/SEP, where, SD is the standard deviation of the concentrations of the validation samples and SEP is the bias-corrected standard error of prediction (for SEP, see Section 4.6 for more details). As a rule of thumb, an RPD ratio lower than 3 suggests the model has poor predictive capabilities [54]. 

Each of the regression models is evaluated for prediction ability, using cross-validation. 

The ability to analyze this data so that its essence can be used to predict or evaluate future cases has barely begun to... 

Using a set of 40 naphthyl-isoquinoline alkaloids, Bringmann and Rummey (56) compared two different methods of generating alignments automatically. Typically, data are divided into a training set used to optimize the parameters of the model and a test set that does not influence the model in any way but is used for evaluation of the predictive abilities of the final model. In this study, all available compounds were used for model development, and given that the researchers synthesized and tested new compounds on a continuous basis, the test set consisted of newly synthesized chemical compounds. When seven newly synthesized compounds were evaluated and predictions were made, a poor correlation was seen between actual and predicted IC50 values (r 0.279). If instead, the results are evaluated as a classification of active or inactive, five of the seven structures are predicted correctly. 

A number of attempts in interpreting trickle-bed performance appeared in the open literature (6-14). These studies did not demonstrate the predictive ability of the proposed reactor models. Some used the reaction data in trickle-beds to evaluate unknown model parameters in order to match calculated and experimental results (7-11). Other studies left certain observed phenomena unexplained (6-12). The objective of this paper is to develop a model for a gas reactant limited reaction in an isothermal trickle-bed reactor. Model parameters are evaluated by independent means and model s predictive ability is tested. 

Quantitative structure-activity/pharmacokinetic relationships (QSAR/ QSPKR) for a series of synthesized DHPs and pyridines as Pgp (type I (100) II (101)) inhibitors was generated by 3D molecular modelling using SYBYL and KowWin programs. A multivariate statistical technique, partial least square (PLS) regression, was applied to derive a QSAR model for Pgp inhibition and QSPKR models. Cross-validation using the leave-one-out method was performed to evaluate the predictive performance of models. For Pgp reversal, the model obtained by PLS could account for most of the variation in Pgp inhibition (R2 = 0.76) with fair predictive performance (Q2 = 0.62). Nine structurally related 1,4-DHPs drugs were used for QSPKR analysis. The models could explain the majority of the variation in clearance (R2 = 0.90), and cross-validation confirmed the prediction ability (Q2 = 0.69) [ 129]. 

Since dissolved gas concentrations in the liquid phase are more difficult to measure experimentally than the liquid reactant concentration, Equation 8 evaluated at the reactor exit 5=1 represents the key equation for practical applications involving this model. Nevertheless, the resulting expression still contains a significant number of parameters, most of which cannot be calculated from first principles. This gives the model a complex form and makes it difficult to use with certainty for predictive purposes. Reaction rate parameters can be determined in a slurry and basket-type reactor with completely wetted catalyst particles of the same kind that are used in trickle flow operation so that DaQ, r 9 and A2 can be calculated for trickle-bed operation. This leaves four parameters (riCE> St gj, Biw, Bid) to be determined from the available contacting efficiency and mass transfer correlations. It was shown that the model in this form does not have good predictive ability (29,34). 

In critically ill patients with AKI, urinary KIM-1 along with N-acetyl-[beta]-(D)-glucosaminidase activity (NAG) showed increasing trends with increasing severity of illness as assessed by Acute Physiology, Age, Chronic Health Evaluation (APACHE) II and multiple organ failure scores and could be correlated to the odds for both renal replacement therapy and hospital death, suggesting these biomarkers have some predictive ability for clinical outcomes in patients with AKI [308]. 

Evaluation of new pharmaceuticals, pesticides, and the like for developmental toxicity is required by law. Such testing is actually a special type of toxicity testing and the rules for these studies are based on a few generally accepted principles and a number of assumptions. The principles are listed in this section and the assumptions implicit in these studies are discussed. The overall predictive ability of the animal studies to give reliable indication of potential adverse effects in humans is then presented. 

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