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This Week's Discoveries | 16 april 2019

  • Kai Ye
  • Jianyong Sun
16 April 2019
This Week's Discoveries
Niels Bohrweg 2
2333 CA Leiden
De Sitterzaal

First lecture

The opium poppy genome and morphinan production

Kai Ye (Xi’an Jiaotong University) is a professor at Xi’an Jiaotong University. He obtained his PhD at Leiden University in 2008 and was an assistant professor at Leiden University Medical Center. Dr Ye is working on computational methods for structural variants as well as their applications on precision medicine and evolution of novel traits.

Morphinan-based painkillers are derived from opium poppy (Papaver somniferum L.). We report a draft of the opium poppy genome, with 2.72 gigabases assembled into 11 chromosomes with contig N50 and scaffold N50 of 1.77 and 204 megabases, respectively. Synteny analysis suggests a whole-genome duplication at ~7.8 million years ago and ancient segmental or whole-genome duplication(s) that occurred before the Papaveraceae-Ranunculaceae divergence 110 million years ago. Syntenic blocks representative of phthalideisoquinoline and morphinan components of a benzylisoquinoline alkaloid cluster of 15 genes provide insight into how this cluster evolved. Paralog analysis identified P450 and oxidoreductase genes that combined to form the STORR gene fusion essential for morphinan biosynthesis in opium poppy. Thus, gene duplication, rearrangement, and fusion events have led to evolution of specialized metabolic products in opium poppy.

Second lecture

Learning from a Stream of Non-Stationary and Dependent Data in Multiobjective Evolutionary Optimization

Jianyong Sun (NEL-BDA, Xi’an Jiaotong University) is the Director of Research Centre for Big Data Analysis Algorithm of National Engineering Laboratory for Big Data Analytics (NEL-BDA), Professor in School of Mathematics and Statistics, Xi’an Jiaotong University, IEEE Senior Member, Visiting Professor in University of Essex, U.K., U.K. HEA Fellow. Research interests include computational intelligence, statistical machine learning, big data algorithm development and analysis. He has published papers in prestigious journals such as Proceedings of the National Academy of Science, IEEE Transactions, and others.

Combining machine learning techniques has shown great potentials in evolutionary optimization since the domain knowledge of an optimization problem, if well learned, can be a great help for creating high-quality solutions. However, existing learning-based multiobjective evolutionary algorithms spend too much computational overhead on learning. To address this problem, we propose a learning-based multiobjective evolutionary algorithm where an {\em online} learning algorithm is embedded within the evolutionary search procedure. The online learning algorithm takes the stream of sequentially generated solutions along the evolution as its training data. It is noted that the stream of solutions are temporal, dependent, non-stationary and non-static. These data characteristics make existing online learning algorithm not suitable for the evolution data. We hence modify an existing online agglomerative clustering algorithm to accommodate these characteristics. The modified online clustering algorithm is applied to adaptively discover the structure of the Pareto optimal set; and the learned structure is used to guide new solution creation. Experimental results have shown significant improvement over four state-of-the-art multiobjective evolutionary algorithms on a variety of benchmark problems.

About Xi’an Jiaotong University

Xi’an Jiaotong University (XJTU) is a key university and one of the oldest universities in China. As a member of C9 League (top 9 in China), 2 disciplines of XJTU are ranked top 10/00 on the ESI list, 14 disciplines of XJTU are ranked top 1% on the ESI list up to March, 2018.

Xi’an Jiaotong University is one of the most valuable partners for Leiden University. Research collaborations are facilitated with, mutual visiting professorship, joint PhD programme and other workshops. Students from both universities can also participate in programmes set up jointly by XJTU and Leiden University.

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