On the Use of MDL Principle in Gene Expression Prediction

The structure and biological behavior of a cell are determined by the pattern of gene expressions within that cell. The so-called gene prediction problem refers to finding rules, or sets of possible rules, on how certain genes expressions determine the expression level of a given target gene. In this paper, we investigate the gene prediction problem and propose the use of new predictors, selected according to the minimum description length (MDL) principle. We compare the use of Boolean predictors, ternary predictors and perceptron predictors. We resort to MDL as a tool for selecting the proper size of the prediction window. MDL is also well suited for comparing predictors having different complexities. We show that the best description can be achieved by the Boolean and ternary predictors, since they obtain better fitting of the data with a lower complexity of the model. To illustrate the comparison, both synthetic and experimental data are used.

• Publication year

  2001

• Venue

  EURASIP Journal on Advances in Signal Processing

• Publication date

  Unknown publication date

• Fields of study

  Biology, Mathematics, Computer Science

• Identifiers
  DOI 10.1155/S1110865701000270
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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