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.

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.

Chuang Ma
Professor, Doctoral Supervisor

My research interests include artificial intelligence, abiotic stress and plant breeding.