The prediction of protein secondary structure with a cascade correlation learning architecture of neural networks

被引：5
|
作者：
Vivarelli, F [1 ]
Fariselli, P [1 ]
Casadio, R [1 ]
机构：
[1] UNIV BOLOGNA, DEPT BIOL, BIOPHYS LAB, I-40126 BOLOGNA, ITALY
来源：
NEURAL COMPUTING & APPLICATIONS | 1997年 / 6卷 / 01期
关键词：
cascade correlation learning algorithm; neural networks; pattern recognition; predictive methods; protein secondary structure prediction;
D O I：
10.1007/BF01670152
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A Cascade Correlation Learning Architecture (CCLA) of neural networks is tested on the task of predicting the secondary structure of proteins. The results are compared with those obtained with Neural Networks (NN) trained with the back-propagation algorithm (BPNN) and generated with genetic algorithms. CCLA proceeds towards rite global minimum of the error function more efficiently than BPNN. However; only a slight improvement in the average efficiency value is noticeable (61.82% as compared with 61.61% obtained with BPNN). The values of the three correlation coefficients for the discriminated secondary structures are also rather similar (C-alpha, and C-beta and C-coil are 0.36, 0.29 and 0.36 with CCLA, and 0.36, 0.31 and 0.35 with BPNN). This indicates that the efficiency of the prediction does not depend upon the training algorithm, and confirms our previous observation that when single sequences are used as input code to the network system.
引用
收藏
页码：57 / 62
页数：6
相关论文
共 50 条
  • [21] An architecture combining Bayesian segmentation and neural network ensembles for protein secondary structure prediction
    Bidargaddi, NP
    Chetty, M
    Kamruzzaman, J
    Proceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, 2005, : 498 - 505
  • [22] PROTEIN SECONDARY STRUCTURE PREDICTION WITH A NEURAL NETWORK
    HOLLEY, LH
    KARPLUS, M
    PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 1989, 86 (01) : 152 - 156
  • [23] Protein Ensemble Learning with Atrous Spatial Pyramid Networks for Secondary Structure Prediction
    Guo, Yuzhi
    Wu, Jiaxiang
    Ma, Hehuan
    Wang, Sheng
    Huang, Junzhou
    2020 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE, 2020, : 17 - 22
  • [24] PEPTIDES SECONDARY STRUCTURE PREDICTION WITH NEURAL NETWORKS - A CRITERION FOR BUILDING APPROPRIATE LEARNING SETS
    RUGGIERO, C
    SACILE, R
    RAUCH, G
    IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 1993, 40 (11) : 1114 - 1121
  • [25] Protein secondary structure prediction using modular reciprocal bidirectional recurrent neural networks
    Babaei, Sepideh
    Geranmayeh, Amir
    Seyyedsalehi, Seyyed Ali
    COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2010, 100 (03) : 237 - 247
  • [26] Prediction of protein secondary structure at high accuracy using a combination of many neural networks
    Lundegaard, Claus
    Petersen, Thomas Nordahl
    Nielsen, Morten
    Bohr, Henrik
    Bohr, Jacob
    Brunak, Søren
    Gippert, Carry
    Lund, Ole
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2003, 2666 : 117 - 122
  • [27] Multifaceted analysis of training and testing convolutional neural networks for protein secondary structure prediction
    Shapovalov, Maxim
    Dunbrack, Roland L., Jr.
    Vucetic, Slobodan
    PLOS ONE, 2020, 15 (05):
  • [28] IGPRED: Combination of convolutional neural and graph convolutional networks for protein secondary structure prediction
    Gormez, Yasin
    Sabzekar, Mostafa
    Aydin, Zafer
    PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS, 2021, 89 (10) : 1277 - 1288
  • [29] Data representation influences protein secondary structure prediction using Artificial Neural Networks
    Lamont, O
    Liang, HH
    Bellgard, M
    ANZIIS 2001: PROCEEDINGS OF THE SEVENTH AUSTRALIAN AND NEW ZEALAND INTELLIGENT INFORMATION SYSTEMS CONFERENCE, 2001, : 411 - 415
  • [30] Protein Secondary Structure Prediction Based on Two Dimensional Deep Convolutional Neural Networks
    Liu, Yihui
    Cheng, Jinyong
    Ma, Yuming
    Chen, Yehong
    PROCEEDINGS OF 2017 3RD IEEE INTERNATIONAL CONFERENCE ON COMPUTER AND COMMUNICATIONS (ICCC), 2017, : 1995 - 1999
12345