Machine learning (ML) is an intelligent data mining technique to recognize patterns in large-scale data sets, the capability of which in Big Data analysis was exemplified in the Go match between Google’s artificial intelligence program AlphaGo and the world-class Go players like Lee Sedol. We presented an ML-based methodology termed mlDNA for large-scale integration analysis of transcriptome data via comparison of gene coexpression networks (Figure 3). mlDNA substantially outperformed traditional statistical testing–based differential expression analysis in identifying stress-related genes, with markedly improved prediction accuracy. Some of the mlDNA predictions have been validated with phenotyping experiments.
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Ma, C., Xin, M., Feldmann, K., Wang, X. (2014). Machine Learning–Based Differential Network Analysis: A Study of Stress-Responsive Transcriptomes in Arabidopsis The Plant Cell 26(2), 520-537. https://dx.doi.org/10.1105/tpc.113.121913
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