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
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Views: 1574
Created: 20th Apr 2020 at 10:42
Last updated: 7th Dec 2021 at 17:58
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