Textual Information for Predicting Functional Properties of the Genes

Oana Frunza, D. Inkpen
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引用次数: 10

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.
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用于预测基因功能特性的文本信息
本文的研究重点是确定哪些蛋白质会影响芳烃受体(Aryl Hydrocarbon Receptor, AHR)系统的活性,同时学习一个能够准确预测单个基因敲除时AHR系统活性的模型。当模型在单一信息源上进行训练时,实验结果就会出现:Medline (http://medline.cos.com/)的摘要,其中讨论了实验中涉及的基因。结果表明,采用二元词袋表示的AdaBoost分类器获得了明显更好的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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