Fanrui Meng

This study presents a haplotype-resolved genome for Phalaenopsis orchids, revealing genetic factors underlying trait diversity such as lip morphology and color patterns.

This study evaluates factors affecting genomic prediction accuracy in switchgrass, finding that exome capture and multi-allelic variants improve predictions for complex traits.

This study uses deep learning to accurately measure Arabidopsis fitness traits, revealing gene function effects previously undetected by traditional methods.

The paper identifies misassembled regions in short-read genome assemblies using coverage variation and machine learning, finding that 27.8% of high coverage regions are potentially misassembled.

This study uses machine learning to distinguish caveolae from scaffolds in SMLM data, achieving high accuracy with random forest and CNN models.