Author

Hannah Bier

Publication Date

2009

Document Type

Honors Thesis

Department

Computer Science

Abstract

In this project, we propose a method for fully automating the analysis of agarose electrophoresis gels. We implement an algorithm to parse the gel image into lanes and bands. Then, we create another algorithm that optimizes the parser's parame- ters for a given gel. Although it is not implemented here, we propose a supervised learning approach (over many gels) to find the optimal values for the optimizer's own input parameters. Once a gel has been parsed, we give an algorithm to ana- lyze each band and we explain how the results could be compared with the given information on the gel's ladder.

Language

English

Comments

57 p. : col. ill. Honors Project-Smith College, Northampton, Mass., 2009. Includes bibliographical references.

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