Show/Hide Menu
Hide/Show Apps
anonymousUser
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Açık Bilim Politikası
Açık Bilim Politikası
Frequently Asked Questions
Frequently Asked Questions
Browse
Browse
By Issue Date
By Issue Date
Authors
Authors
Titles
Titles
Subjects
Subjects
Communities & Collections
Communities & Collections
Using artificially generated spectral data to improve protein secondary structure prediction from Fourier transform infrared spectra of proteins
Date
2004-09-15
Author
Severcan, M
Haris, PI
Severcan, Feride
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
2
views
0
downloads
Secondary structures of proteins have been predicted using neural networks from their Fourier transform infrared spectra. To improve the generalization ability of the neural networks, the training data set has been artificially increased by linear interpolation. The leave-one-out approach has been used to demonstrate the applicability of the method. Bayesian regularization has been used to train the neural networks and the predictions have been further improved by the maximum-likelihood estimation method. The networks have been tested and standard error of prediction (SEP) of 4.19% for alpha helix, 3.49% for beta sheet, and 3.15% for turns have been achieved. The results indicate that there is a significant decrease in the SEP for each type of structure parameter compared to previous works.
Subject Keywords
Biophysics
,
Cell Biology
,
Biochemistry
,
Molecular Biology
URI
https://hdl.handle.net/11511/56922
Journal
ANALYTICAL BIOCHEMISTRY
DOI
https://doi.org/10.1016/j.ab.2004.06.030
Collections
Department of Biology, Article