The naive Bayes approach has several well-known difficulties. The conditional independence of descriptors of a molecule structure is not true as a rule. The probability P A Di) estimations can be close or even equal to 0 or 1 and in such case coefficients a,- become too large or infinite. To overcome this problem, we have substituted the logarithms of probabilities ratios n[P A D j —P A Di )] for ArcSin(2P(T Z),)—1). The ArcSin(2P( Z),)—1) shape coincides with the shape of ln[P(T Z),)/(l—P( Ai))] for almost all values of P(A Z),), but ArcSin(2P( P)i)—1) values are bounded by the values ti/2. [Pg.194]

Watson, P. Naive Bayes classification using 2D pharmacophore feature triplet vectors. J. Chem. Inf. Model. 2008,48,166-78. [Pg.215]

Srm, H. (2005) A naive Bayes classifier for prediction of mrrltidrrrg resistance reversal activity on the basis ofatomtypirrg./. Med. Chem., 48,4031—4039. [Pg.1176]

Interestingly, the naive Bayes approach is too simple , but as a rule it provides high accuracy of recognition. [Pg.194]

Sun [54] reported a naive Bayes classifier built around a training set of 1979 compounds with measured hERG activity from the Roche corporate collection. For the training set, 218 in-house atom-type descriptors were used to develop the model, and pICso = 4.52 was set as a threshold between hERG actives and inactives. Receiver operator curve (ROC) accuracy of 0.87 was achieved. The model was validated on an external set of 66 drugs, of which 58 were classified correctly (88% accuracy). [Pg.361]

The probabUistic model for a naive Bayes classifier is a conditional model P(T Xi, X2,..., X ) over a dependent class variable F, conditional on features Xi, X2, X. Using Bayes s theorem, F(F Xj,..., X ) oc P(F) 7(Xi,..., X F). The prior probability F(F = j) can be calculated based on the ratio of the class j samples such as P(F = 7) = (number of class j samples)/(total number of samples). Having formulated the prior probabihties, the likelihood function p(Xi, X2,..., X F) can be written as ]/[ j p(Xi F) under the naive conditional independence assumptions of the feature X, with the feamre Xj for j i. A new sample is classified to a class with maximum posterior probability, which is argmaxr erF (r7)nr ( i 1 /)- If the independence assumption is correct, it is the Bayes optimal classifier for a problem. Extensions of the naive Bayes classifier can be found in Demichelis et al. (2006). [Pg.132]

When applied to virtual screening the naive Bayes classifier consists in the following. [Pg.193]

Diverse Three TAACF datasets from PubChem 179 Naive Bayes, random forest, sequential minimal optimization, J48 decision tree. Used to create three models with different datasets. Naive Bayes had external test set accuracy 73-82.7, random forest 60.7-82.7%, SMO 55.9-83.3, and J48 61.3-80% Periwal et al. (36, 37) [Pg.249]

Klon, A.E., Glick, M. and Davies, J.W (2004) Combination of a naive Bayes classifier with consensus scoring improves enrichment of high-throughput docking results. /. Med. Chem., 47, 4356-4359. [Pg.1094]

Data Discovery, bioinformatics and cheminformatics, and called naive Bayes [Pg.193]

Epitopia Physicochemical and structural geometrical features with Naive Bayes http //epitopia.tau.ac.il/ Rubinstein et al. (38) [Pg.134]

Rennie, J. D. M. 2001. Improving multi-class text classification with Naive Bayes. MA thesis. Massachusetts Institute of Technology. [Pg.191]

Cao, J., Panetta, R., Yue, S., Steyaert, A., Young-BelKdo, M. and Ahmad, S. (2003) A naive Bayes model to predict coupling between seven transmembrane domain receptors and G-proteins. Bioinformatics 19, 234-240. [Pg.54]

Demichelis, R, Magni, R, Piergiorgi, R, Rubin M. A., and Bellazzi, R. (2006). A hierarchical naive Bayes model for handling sample heterogeneity in classification problems An application to tissue micioarrays. BMC Bioinformatics, 7 514. [Pg.154]

Technische Universitat Wien Does latent class analysis, short-time Fourier transform, fuzzy clustering, support vector machines, shortest path computation, bagged clustering, naive Bayes classifier, etc. (http //cran.r-project.org/ web/packages/el071/index.html) [Pg.24]

Clearly, the constant can be included into threshold value B, so that the function /o(C) = 1 is not necessary. We must stress that in such form the probabilistic approach has no tuned parameters at all. Some tuning of naive Bayes classifier can be performed by selection of the molecular structure descriptors [or /(C)] set. This is a wonderful feature in contrast to QSAR methods, especially to Artificial Neural Networks. [Pg.194]

Another work aims at classifying candidate correspondences (either as relevant or not) by analysing their features [Naumann et al. 2002], The features represent boolean properties over data instance, such as presence of delimiters. Thus, selecting an appropriate feature set is a first parameter to deal with. The choice of a classifier is also important, and authors propose, by default, the Naive Bayes classifier for categorical data and quantile-based classifier for numerical data. [Pg.299]

See also in sourсe #XX -- [ Pg.178 , Pg.185 , Pg.209 , Pg.210 ]

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