Analysis of Large-Scale OMIC Data Using Self Organizing Maps

Abstract:

The data produced by high-throughput bioanalytics is usually given as a feature matrix of dimension N x M (see Figure 1) where N is the number of features measured per sample and M is the number of samples referring, e.g., to different treatments, time points or individuals. As a convention, each row of the matrix will be termed profile of the respective feature. The columns on the other hand will be termed states referring to each of the conditions studied. In general, the number of features can range from several thousands to millions, depending on the experimental screening technique used. Typically, this number largely exceeds the number of states studied, i.e. N>>M. SOM machine learning aims at reducing the number of relevant features by grouping the input data into clusters of appropriate size, and thus to transform the matrix of input data into a matrix of so-called meta-data with a reduced number of meta-features, K<

DOI: 10.4018/978-1-4666-5888-2.ch157

Projects: LHA - Leipzig Health Atlas

Publication type: Not specified

Journal: Encyclopedia of Information Science and Technology, Third Edition, IGI Global, 1642-1653

Human Diseases: No Human Disease specified

Citation:

Date Published: 2015

Registered Mode: Not specified

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Citation
Binder, H., & Wirth, H. (2014). Analysis of Large-Scale OMIC Data Using Self Organizing Maps. In Advances in Information Quality and Management (pp. 1642–1653). IGI Global. https://doi.org/10.4018/978-1-4666-5888-2.ch157
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Created: 20th Apr 2020 at 10:42

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