A Machine-Learning Strategy for Protein Analysis

Authors:
Pierre Baldi;Gianluca Pollastri
Affiliations:
-;-
Venue:
IEEE Intelligent Systems
Year:
2002

Citing 2
Cited 5

Bioinformatics: the machine learning approach

Bioinformatics: the machine learning approach
Matching Protein b-Sheet Partners by Feedforward and Recurrent Neural Networks

Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology

The principled design of large-scale recursive neural network architectures--dag-rnns and the protein structure prediction problem

The Journal of Machine Learning Research
Multi-class pattern classification using neural networks

Pattern Recognition
A simplified multi-class support vector machine with reduced dual optimization

Pattern Recognition Letters
Structural proteomics of the poxvirus family

Artificial Intelligence in Medicine
A Highly Parallel Multi-class Pattern Classification on GPU

CCGRID '12 Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)

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Abstract

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.