A Family of Feed-Forward Models for Protein Sequence Classification
Abstract
Advances in sequencing have greatly outpaced experimental methods for determining a protein’s structure and function. As a result, biologists increasingly rely on computational techniques to infer these properties of proteins from sequence information alone. We present a sequence classification framework that differs from the common SVM/kernel-based approach. We introduce a type of artificial neural network which we term the Subsequence Network (SN) that incorporates structural models over sequences in its lowest layer. These structural models, which we call Sequence Scoring Models (SSM), are similar to Hidden Markov Models and act as a mechanism to extract relevant features from sequences. In contrast to SVM/kernel methods, which only allow learning of linear discrimination weights, our feed-forward structure allows linear weights to be learned in conjunction with sequence-level features using standard optimization techniques.
Cite
Text
Blasiak et al. "A Family of Feed-Forward Models for Protein Sequence Classification." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2012. doi:10.1007/978-3-642-33486-3_27Markdown
[Blasiak et al. "A Family of Feed-Forward Models for Protein Sequence Classification." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2012.](https://mlanthology.org/ecmlpkdd/2012/blasiak2012ecmlpkdd-family/) doi:10.1007/978-3-642-33486-3_27BibTeX
@inproceedings{blasiak2012ecmlpkdd-family,
title = {{A Family of Feed-Forward Models for Protein Sequence Classification}},
author = {Blasiak, Sam and Rangwala, Huzefa and Laskey, Kathryn B.},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2012},
pages = {419-434},
doi = {10.1007/978-3-642-33486-3_27},
url = {https://mlanthology.org/ecmlpkdd/2012/blasiak2012ecmlpkdd-family/}
}