Textual information for predicting functional properties of the genes

Authors:
Oana Frunza;Diana Inkpen
Affiliations:
University of Ottawa, Ottawa, ON, Canada;University of Ottawa, Ottawa, ON, Canada
Venue:
BioNLP '08 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
Year:
2008

Citing 3
Cited 0

One class SVM for yeast regulation prediction

ACM SIGKDD Explorations Newsletter
Predicting the effects of gene deletion

ACM SIGKDD Explorations Newsletter
Combining data and text mining techniques for yeast gene regulation prediction: a case study

ACM SIGKDD Explorations Newsletter

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper is focused on determining which proteins affect the activity of Aryl Hydrocarbon Receptor (AHR) system when learning a model that can accurately predict its activity when single genes are knocked out. Experiments with results are presented when models are trained on a single source of information: abstracts from Medline (http://medline.cos.com/) that talk about the genes involved in the experiments. The results suggest that AdaBoost classifier with a binary bag-of-words representation obtains significantly better results.