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A Machine-Learning Strategy for Protein Analysis
March/April 2002 (vol. 17 no. 2)
pp. 28-35
ASCII Text
x 
 
Pierre Baldi, Gianluca Pollastri, "A Machine-Learning Strategy for Protein Analysis," IEEE Intelligent Systems, vol. 17, no. 2, pp. 28-35, March/April, 2002.
 
 
BibTex
x
 
@article{ 10.1109/MIS.2002.10004,
author = {Pierre Baldi and Gianluca Pollastri},
title = {A Machine-Learning Strategy for Protein Analysis},
journal ={IEEE Intelligent Systems},
volume = {17},
number = {2},
issn = {1541-1672},
year = {2002},
pages = {28-35},
doi = {http://doi.ieeecomputersociety.org/10.1109/MIS.2002.10004},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - MGZN
JO - IEEE Intelligent Systems
TI - A Machine-Learning Strategy for Protein Analysis
IS - 2
SN - 1541-1672
SP28
EP35
EPD - 28-35
A1 - Pierre Baldi,
A1 - Gianluca Pollastri,
PY - 2002
KW - contact map
KW - evolutionary information
KW - protein structure prediction
KW - protein contacts
KW - recurrent neural networks
KW - secondary structure
KW - solvent accessibility
VL - 17
JA - IEEE Intelligent Systems
ER -
 

The authors provide a brief overview of the application of machine-learning methods to proteomic problems. They outline a novel strategy for the complete prediction of protein 3D coordinates. The strategy relies on three main successive stages: prediction of structural features, topology, and actual coordinates.

Index Terms:
contact map, evolutionary information, protein structure prediction, protein contacts, recurrent neural networks, secondary structure, solvent accessibility
Citation:
Pierre Baldi, Gianluca Pollastri, "A Machine-Learning Strategy for Protein Analysis," IEEE Intelligent Systems, vol. 17, no. 2, pp. 28-35, March-April 2002, doi:10.1109/MIS.2002.10004
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