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Prediction Ability (prediction + ability)
Selected AbstractsModeling based on subspace orthogonal projections for QSAR and QSPR researchJOURNAL OF CHEMOMETRICS, Issue 1 2008Yizeng Liang Abstract A novel projection modeling method for quantitative structure activity relationship (QSAR) and quantitative structure property relationship (QSPR) is developed in this paper. Orthogonalization of block variables is introduced to deal with the problem of variable selection. Projections based on least squares are used to construct the modeling space in order to search for the best regression directions for chemical modeling. A suitable prediction space for such a model is further defined to confine the usage range of the model. Three real data sets were analyzed to check the performance of the proposed modeling method. The results obtained from Monte-Carlo cross-validation (MCCV) showed that the proposed modeling method might provide better results for QSAR and QSPR modeling than PCR and PLS with respect to both fitting and prediction abilities. Copyright © 2007 John Wiley & Sons, Ltd. [source] Predictive and discriminating three-risk-group prognostic scoring system for staging Hodgkin lymphomasCANCER, Issue 2 2007Delphine Maucort-Boulch MD Abstract BACKGROUND. Several 3-stage Ann Arbor classification-derived prognostic systems were constructed since 1980 to identify the prognosis of Hodgkin lymphoma (HL). Modern statistical tools were applied to 955 patients treated between 1981 and 1996 to build a 3-stage prognostic scoring system (PSS). METHODS. Each variable associated with 10-year overall survival (10-year OS) was assigned to 2 (0 or 1) or 3 (0, 1 or 3) values. By summing the values attributed to each variable, 3 stages were defined. 10-year OS, 5-year event-free survival (5-year EFS), and freedom from progression (5-year FFP) rates of the PSS and of other existing systems were then compared. RESULTS. Four variables were associated with 10-year OS: age (<40 = 0, ,40 = 1), number of involved lymphoid areas (1,2 = 0, 3,4 = 1, ,5 = 2), visceral disease (no = 0, yes = 1), and systemic symptoms (no = 0, yes = 1). Scores 0 and 1, 2 and 3, and ,4 were attributed to 59.7%, 30.9%, and 9.4% of the patients who had 10-year OS rates of 93.5, 75.7, and 53.4% and 5-year EFS / 5-year FFP rates of 91.2%/90.3%, 78.1%/76.3%, and 54.1%/52.6%, respectively. The discrimination and prediction abilities of the PSS were better than those of the other systems tested; moreover, the PSS adequately identified the few patients with a worse prognosis without resorting to the International Prognostic Score for advanced stages. The PSS was also highly predictive for 489 patients treated between 1997 and 2002. CONCLUSION. PSS is a useful alternative to the existing prognostic systems for evaluating HL patients. Cancer 2007. © 2006 American Cancer Society. [source] Relative accuracy and predictive ability of direct valuation methods, price to aggregate earnings method and a hybrid approachACCOUNTING & FINANCE, Issue 4 2006Lucie Courteau M41 Abstract In this paper, we assess the relative performance of the direct valuation method and industry multiplier models using 41 435 firm-quarter Value Line observations over an 11 year (1990,2000) period. Results from both pricing-error and return-prediction analyses indicate that direct valuation yields lower percentage pricing errors and greater return prediction ability than the forward price to aggregated forecasted earnings multiplier model. However, a simple hybrid combination of these two methods leads to more accurate intrinsic value estimates, compared to either method used in isolation. It would appear that fundamental analysis could benefit from using one approach as a check on the other. [source] A modified support vector machine based prediction model on streamflow at the Shihmen Reservoir, TaiwanINTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 8 2010Pei-Hao Li Abstract The uncertainty of the availability of water resources during the boreal winter has led to significant economic losses in recent years in Taiwan. A modified support vector machine (SVM) based prediction framework is thus proposed to improve the predictability of the inflow to Shihmen reservoir in December and January, using climate data from the prior period. Highly correlated climate precursors are first identified and adopted to predict water availability in North Taiwan. A genetic algorithm based parameter determination procedure is implemented to the SVM parameters to learn the non-linear pattern underlying climate systems more flexibly. Bagging is then applied to construct various SVM models to reduce the variance in the prediction by the median of forecasts from the constructed models. The enhanced prediction ability of the proposed modified SVM-based model with respect to a bagged multiple linear regression (MLR), simple SVM, and simple MLR model is also demonstrated. The results show that the proposed modified SVM-based model outperforms the prediction ability of the other models in all of the adopted evaluation scores. Copyright © 2009 Royal Meteorological Society [source] Study of saline wastewater influence on activated sludge flocs through automated image analysisJOURNAL OF CHEMICAL TECHNOLOGY & BIOTECHNOLOGY, Issue 4 2009Daniela P Mesquita Abstract BACKGROUND: In activated sludge systems, sludge settling ability is considered a critical step in effluent quality and determinant of solid,liquid separation processes. However, few studies have reported the influence of saline wastewater on activated sludge. This work aims the evaluation of settling ability properties of microbial aggregates in a sequencing batch reactor treating saline wastewaters of up to 60 g L,1 NaCl, by image analysis procedures. RESULTS: It was found that the sludge volume index (SVI) decreased with salt content up to 20 g L,1, remaining somewhat stable above this value. Furthermore, it was found that between the first salt concentration (5 g L,1) and 20 g L,1 aggregates suffered a strong deflocculation phenomenon, leading to a heavy loss of aggregated biomass. Regarding SVI prediction ability, a good correlation coefficient of 0.991 between observed and predicted SVI values was attained. CONCLUSION: From this work the deflocculation of aggregated biomass with salt addition due to pinpoint floc formation, dispersed bacteria growth and protozoa absence could be established. With respect to SVI estimation, and despite the good correlation obtained, caution is advisable given the low number of SVI data points. Copyright © 2008 Society of Chemical Industry [source] O2-PLS for qualitative and quantitative analysis in multivariate calibrationJOURNAL OF CHEMOMETRICS, Issue 6 2002Johan Trygg Abstract In this paper the O-PLS method [1] has been modified to further improve its interpretational functionality to give (a) estimates of the pure constituent profiles in X as well as model (b) the Y-orthogonal variation in X, (c) the X-orthogonal variation in Y and (d) the joint X,Y covariation. It is also predictive in both ways, X , Y. We call this the O2-PLS approach. In earlier papers we discussed the improved interpretation using O-PLS compared to the partial least squares projections to latent structures (PLS) when systematic Y-orthogonal variation in X exists, i.e. when a PLS model has more components than the number of Y variables. In this paper we show how the parameters in the PLS model are affected and to what degree the interpretational ability of the PLS components changes with the amount of Y-orthogonal variation. In both real and synthetic examples, the O2-PLS method provided improved interpretation of the model and gave a good estimate of the pure constituent profiles, and the prediction ability was similar to the standard PLS model. The method is discussed from geometric and algebraic points of view, and a detailed description of this modified O2-PLS method is given and reviewed. Copyright © 2002 John Wiley & Sons, Ltd. [source] Quantitative prediction of protein,protein binding affinity with a potential of mean force considering volume correctionPROTEIN SCIENCE, Issue 12 2009Yu Su Abstract Quantitative prediction of protein,protein binding affinity is essential for understanding protein,protein interactions. In this article, an atomic level potential of mean force (PMF) considering volume correction is presented for the prediction of protein,protein binding affinity. The potential is obtained by statistically analyzing X-ray structures of protein,protein complexes in the Protein Data Bank. This approach circumvents the complicated steps of the volume correction process and is very easy to implement in practice. It can obtain more reasonable pair potential compared with traditional PMF and shows a classic picture of nonbonded atom pair interaction as Lennard-Jones potential. To evaluate the prediction ability for protein,protein binding affinity, six test sets are examined. Sets 1,5 were used as test set in five published studies, respectively, and set 6 was the union set of sets 1,5, with a total of 86 protein,protein complexes. The correlation coefficient (R) and standard deviation (SD) of fitting predicted affinity to experimental data were calculated to compare the performance of ours with that in literature. Our predictions on sets 1,5 were as good as the best prediction reported in the published studies, and for union set 6, R = 0.76, SD = 2.24 kcal/mol. Furthermore, we found that the volume correction can significantly improve the prediction ability. This approach can also promote the research on docking and protein structure prediction. [source] | ||||||||||