Isbn: 9783844313239 - protein secondary structure prediction: helical transmembrane region prediction using adaptive neuro-fuzzy inference system (8 Ergebnisse)

Zustand: New. pp. 64.

Taschenbuch. Zustand: Neu. Protein Secondary Structure Prediction | Helical transmembrane region prediction using Adaptive Neuro-Fuzzy Inference System | Indu Khatri (u. a.) | Taschenbuch | 64 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783844313239 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.…

Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Over last many years, various computational models have been applied for solving various biological problems. Various transmembrane region predictors have been developed for prediction of secondary structure of proteins with varying levels of accuracy. This book provides Adaptive Neuro-Fuzzy Inference System (ANFIS) based model for predicting helical transmembrane region by acquiring the structural information of target protein directly from its sequence data. Also, a Fuzzy Inference System was developed using the same test set for comparing the performance of ANFIS model. The best configuration of ANFIS model with RMSE and accuracy as 40.92% and 76.76%, respectively, seems to outperform the fuzzy model, which attained RMSE and accuracy as 54.45% and 70.37%, respectively. The success of this approach suggests that it can find potential applications in other sequence-based analysis problems. The model decsribed in this book will be useful to bioinformaticians developing new and challenging models for solving different biological problems. 64 pp. Englisch.…

Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Over last many years, various computational models have been applied for solving various biological problems. Various transmembrane region predictors have been developed for prediction of secondary structure of proteins with varying levels of accuracy. This book provides Adaptive Neuro-Fuzzy Inference System (ANFIS) based model for predicting helical transmembrane region by acquiring the structural information of target protein directly from its sequence data. Also, a Fuzzy Inference System was developed using the same test set for comparing the performance of ANFIS model. The best configuration of ANFIS model with RMSE and accuracy as 40.92% and 76.76%, respectively, seems to outperform the fuzzy model, which attained RMSE and accuracy as 54.45% and 70.37%, respectively. The success of this approach suggests that it can find potential applications in other sequence-based analysis problems. The model decsribed in this book will be useful to bioinformaticians developing new and challenging models for solving different biological problems.…

Zustand: New. Print on Demand pp. 64 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam.

Zustand: New. PRINT ON DEMAND pp. 64.

Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Khatri InduIndu Khatri, M.Sc.: Studied Bioinformatics at Banasthali University, Rajasthan, India. Dr. A. K. Sharma, PhD: Studied Computer Science at Thapar University(Thapar Institute of Engineering & Technology), Punjab, India. Sen.…

Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Over last many years, various computational models have been applied for solving various biological problems. Various transmembrane region predictors have been developed for prediction of secondary structure of proteins with varying levels of accuracy. This book provides Adaptive Neuro-Fuzzy Inference System (ANFIS) based model for predicting helical transmembrane region by acquiring the structural information of target protein directly from its sequence data. Also, a Fuzzy Inference System was developed using the same test set for comparing the performance of ANFIS model. The best configuration of ANFIS model with RMSE and accuracy as 40.92% and 76.76%, respectively, seems to outperform the fuzzy model, which attained RMSE and accuracy as 54.45% and 70.37%, respectively. The success of this approach suggests that it can find potential applications in other sequence-based analysis problems. The model decsribed in this book will be useful to bioinformaticians developing new and challenging models for solving different biological problems.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 64 pp. Englisch.…