AgronomyEN

Our research is mainly based on statistical theory and inference, statistical machine learning theory and methods. In addition, we connect the other divisions’ research through interdisciplinary cooperation. Furthermore, we study the innovation and research of problem-solving-oriented biological statistics and bioinformatics methods.

1. Gene Set Enrichment Analysis (GSEA): In recent years, due to the development of microarray experiments and high-throughput sequencing, various types of genomic data have grown rapidly. GSEA method is mainly applied in genomic data. It’s a method to detect gene clusters related to metabolic pathways or cell regulatory structure that is significantly related to a disease or biological feature. Our major research is to provide a more flexible and robust statistical analysis model. It can be used to detect significant gene clusters related to multiple types and traits and to develop R package for researchers.

2. Genomic Selection: Genomic selection is an emerging marker-assisted selection strategy. Through the statistical prediction model built by training population, it can be used to estimate individual breeding value from massive marker-assisted selection data and to select individual. However, there are still some important problems in marker-assisted selection, such as the difficulty of predicting complex quantitative traits, interaction between genotype and multiple environments, how to balance the effects of major genes and minor genes, the lack of precision in phenotype identification, and the high cost.

3. Statistical Machine Learning:Machine learning algorithms play an important role in many research and application fields of data mining. Classification algorithms have been extensively explored and applied in various fields and data, such as numeric, image analysis, gene expression, word data, etc. However, when facing massive, various, and asymmetrical data analysis, traditional classification algorithms are often insufficient. Therefore, it’s necessary to consider the data heterogeneity and complex structures, develop new statistical machine learning methods and study the construction of model prediction and decision support systems.

Academic Degrees

Experience

International Academic Participation