Automatically assigned DDC number: 00631

Manually assigned DDC number: 006312

Number of references: 8

Title: Recognizing Promoters in DNA Using Bayesian Neural Networks

Author:

Author:

Subject: Qicheng Ma,Jason T. L. Wang Recognizing Promoters in DNA Using Bayesian Neural Networks

Description: Binary data classification is to recognize positive data from unlabeled test data that may contain both positive and negative data. In this paper we propose a two-level approach to recognize E. Coli promoters in unlabeled DNA containing both promoter and nonpromoter sequences. The first-level classifiers include three Bayesian neural networks which learn from three different feature sets. The outputs of the first-level classifiers are combined in the second-level to give the final result. Empirical study shows an excellent performance of the proposed approach. Keywords: computational biology and medicine, data mining and classification, neural networks, promoter recognition. 1 Introduction As a result of the ongoing Human Genome Project, DNA or protein data are accumulated at a speed growing at an exponential rate. Mining these biological data to extract significant information becomes extremely important in accelerating genome processing. Classification, or supervised learning, is o...

Contributor: The Pennsylvania State University CiteSeer Archives

Publisher: unknown

Date: 1999-03-22

Pubyear: 1999

Format: ps

Identifier: http://citeseer.ist.psu.edu/174424.html

Source: http://www-ec.njit.edu/~qxm3943/promoter.ps

Language: en

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Rights: unrestricted

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