Accuracy, neural network algorithm

The basic information of protein tertiary structural class can help improve the accuracy of secondary structure prediction (Kneller et al., 1990). Chandonia and Karplus (1995) showed that information obtained from a secondary structure prediction algorithm can be used to improve the accuracy for structural class prediction. The input layer had 26 units coded for the amino acid composition of the protein (20 units), the sequence length (1 unit), and characteristics of the protein (5 units) predicted by a separate secondary structure neural network. The secondary structure characteristics include the predicted percent helix and sheet, the percentage of strong helix and sheet predictions, and the predicted number of alterations between helix and sheet. The output layer had four units, one for each of the tertiary super classes (all-a, all-p, a/p, and other). The inclusion of the single-sequence secondary structure predictions improved the class prediction for non-homologous proteins significantly by more than 11%, from a predictive accuracy of 62.3% to 73.9%. 

BBB) permeation. Both tools are based on artificial neural networks, with prediction accuracies of approx. 86% and 82%, respectively. For BBB permeation prediction, a novel substructure analysis also provided valuable information regarding the crucial properties for BBB permeation-positive compounds. Today, computer-based algorithms (as presented here) are essential, and integrated elements within the dmg discovery and development process and will help to meet the new challenges of the post-genomic era. 

In this paper, a plurality of data of analysis of ground water quality test were pre-processed with Immune Algorithm (lA). The key characteristics of coal mine water inrush source data are extracted by characteristic analysis method. The complexity of the data is reduced by reducing the dimensionality of the data set. With the help of the data after dimension reduction, the Back Propagation Neural Network (BPNN) is trained. The coal mine water inrush source is recognized by the trained BPNN. Experiments show that if the source of mine disaster water is identified by the method developed in the paper, its accuracy can reach 93%. The more detailed introduction on the method is given below. 

As we might expeet by this stage in the book, most of the usual classification/regression algorithms have been applied to the duration predietion problem. These include decision trees [372], neural networks for phone prediction [109], [157], genetic algorithms [319] and Bayesian belief networks [182], Comparative studies of deeision trees and neural networks found little difference in accuracy between either approaeh, [473], [187], [72],... 

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