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[Regular Paper] Inference of Genetic Networks Using Random Forests: Use of Different Weights for Time-Series and Static Gene Expression Data
- Source :
- 2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE).
- Publication Year :
- 2018
- Publisher :
- IEEE, 2018.
-
Abstract
- Genetic network inference methods using random forests have shown promise. Some of the random-forest-based inference methods have an ability to analyze both time-series and static gene expression data. We think however that, as the gene expression levels at two adjacent measurements of a time-series data are often similar to each other, the gene expression levels at each measurement in the time-series data are less informative than those in the static data. On the basis of this idea, we proposed a new inference method that relies more on static gene expression data than time-series ones. Through the numerical experiments, we showed that the quality of the inferred genetic network is slightly improved by giving greater importance to static data than time-series ones. Although we develop the new method by modifying the random-forest-based inference method proposed by the authors, we could introduce the idea in this study into any inference method that is capable of analyzing both time-series and static gene expression data.
- Subjects :
- 0301 basic medicine
Series (mathematics)
Basis (linear algebra)
Computer science
media_common.quotation_subject
Genetic network
Inference
computer.software_genre
Random forest
03 medical and health sciences
030104 developmental biology
Quality (business)
Data mining
Static data
computer
media_common
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)
- Accession number :
- edsair.doi...........76dffa355a1307f3cb0fdf4fe2929a7e