By Hugh M. Cartwright B.Sc, PhD (auth.), David J. Livingstone (eds.)
As an extension of man-made intelligence study, synthetic neural networks (ANN) target to simulate clever habit via mimicking the way in which that organic neural networks functionality. In Artificial Neural Networks, a world panel of specialists record the historical past of the applying of ANN to chemical and organic difficulties, offer a consultant to community architectures, education and the extraction of principles from proficient networks, and canopy many state of the art examples of the appliance of ANN to chemistry and biology. within the culture of the hugely profitable Methods in Molecular Biology™ sequence, this quantity shows transparent, easy-to-use details with many step by step laboratory protocols.
Comprehensive and cutting-edge, Artificial Neural Networks is a wonderful advisor to this accelerating technological box of study.
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Additional info for Artificial Neural Networks: Methods and Applications
Some argue  that a nonlinear (quadratic) PLS is a safer and more reliable way of assessing nonlinear effects and cross-terms. However, by modifying the standard back-propagation neural network that has most widely been used for QSAR by inclusion of a regularization step incorporating Bayesian statistics, the benefits of neural networks can be retained and almost all the disadvantages removed. A Bayesian regularized artificial neural network [3, 5] (BRANN), coupled with molecular descriptors that relate easily to chemical features, is an excellent approximation to this ideal neural network.
This symptom occurs in 10% of all people and 100% of people with kidney cancer. 0002% of all people. 002%. That is, you still have a very low probability of kidney cancer. The reason is that the symptom of MH is relatively common in the healthy population. If the incidence of MH in the general population were much lower, say one hundredth of 1%, then MH would be a much more powerful indicator of kidney cancer. 02 or 2%. Bayes’ theorem can be used to optimally control regularization and make ANNs more robust, parsimonious, and interpretable.
Relative Independence of Model on Neural Net Architecture This example uses of an extension of a toxicity data set reported and analyzed by us . In this model, we use the CIMI indices  (another type of Burden eigenvalue index) together with charge fingerprint descriptors . 2 and Fig. 2 shows the result of a BRANN calculation with an increasing number of hidden-layer neurodes. It can be seen that increasing the number of neurodes from three to nine makes essentially no difference to the statistics of the training or test set.
Artificial Neural Networks: Methods and Applications by Hugh M. Cartwright B.Sc, PhD (auth.), David J. Livingstone (eds.)