Assessment of Gaussian radial basis function network on protein secondary structures

IEEE23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society --25 October 2001 through 28 October 2001 -- Istanbul --Studies of the radial basis function (RBF) network on protein secondary structures are presented. Secondary structure prediction is a useful first step in understanding how the amino acid sequence of protein determines the native state. If the secondary structure is known, it is possible to derive a comparatively small number of tertiary structures using the secondary structural element pack. A study of the Gaussian-RBF with different window sizes on the dataset developed by Qian-Sejnowski, and also a dissimilar dataset by Chandonia is given. The RBF network predicts each position in turn based on a local window of residues, by sliding this window along the length of the sequence. It is shown that the Gaussian RBF network is not an appropriate technique to be used in the prediction of secondary structure for sequence structural state

Intelligent regression techniques for non-exercise prediction of VO 2max [Akilli regresyon teknikleri kullanarak egzersize dayali olmayan VO 2max Tahmini]

2013 21st Signal Processing and Communications Applications Conference, SIU 2013 --24 April 2013 through 26 April 2013 -- Haspolat --The purpose of this study is to develop nonexercise (N-Ex) VO 2max prediction models by using Support Vector Regression (SVR) and Multilayer Feed Forward Neural Networks (MFFNN). VO2max values of 100 subjects are measured using a maximal graded exercise test. The variables; gender, age, body mass index (BMI), perceived functional ability (PFA) to walk, jog or run given distances and current physical activity rating (PA-R) are used to build two N-Ex prediction models. Using 10-fold cross validation on the dataset, standard error of estimates (SEE) and multiple correlation coefficients (R) of both models are calculated. The MFFNN-based model yields lower SEE (3.23 ml•kg-1•min-1) whereas the SVR-based model yields higher R (0.93). Compared with the results of the other NEx prediction models in literature that are developed using Multiple Linear Regression Analysis, the reported values of SEE and R in this study are considerably more accurate. © 2013 IEEE

Mahalanobis distance with radial basis function network on protein secondary structures

Proceedings of the 2002 IEEE Engineering in Medicine and Biology 24th Annual Conference and the 2002 Fall Meeting of the Biomedical Engineering Society (BMES / EMBS) --23 October 2002 through 26 October 2002 -- Houston, TX --In this paper, the radial basis function (RBF) network method with the Mahalanobis distance was applied to predict the content of protein secondary structure elements. A study of the Mahalanobis-RBF with different window sizes on the dataset developed by Qian-Sejnowski is given. The RBF network predicts each position in turn based on a local window of residues, by sliding this window along the length of the sequence. Comparison of Gaussian-RBF and Mahalanobis-RBF on the Qian dataset shows that the Mahalanobis distance in using RBF gives better results in the prediction of secondary structure for local sequence structural state

Neural network based VO2max prediction models using maximal exercise and non-exercise data [Maksimal egzersiz ve egzersize dayali olmayan verileri kullanarak sinir agi tabanli VO2MAX tahmin modelleri]

2013 21st Signal Processing and Communications Applications Conference, SIU 2013 --24 April 2013 through 26 April 2013 -- Haspolat --Artificial Neural Network (ANN) models based on maximal and non-exercise (N-Ex) variables are developed to predict maximal oxygen uptake (VO 2max) the input variables of the dataset are gender, age, body mass index (BMI), grade, selfreported rating of perceived exertion (RPE) from treadmill test, heart rate (HR), perceived functional ability (PFA) and physical activity rating (PA-R). The performance of the models is evaluated by calculating their standard error of estimate (SEE) and multiple correlation coefficient (R). The results suggest that the performance of VO2max prediction models based on maximal and standard N-Ex variables (i.e. gender, age, BMI etc) can be improved by including questionnaire variables (PFA and PA-R) in the models. © 2013 IEEE