Chengzhi Liang

CLCNet: a contrastive learning and chromosome-aware network for genomic prediction in plants

CLCNet improves genomic prediction accuracy in plants by integrating contrastive learning and chromosome-aware feature selection, outperforming existing models across four crop species and ten traits.

Assembly of chromosome-scale contigs by efficiently resolving repetitive sequences with long reads

This paper introduces HERA, a method that improves genome assembly by resolving repetitive sequences with long reads, resulting in higher contiguity and completeness of reference genomes.

Genome structure and evolution of Antirrhnum majus L.

This study presents the first high-quality genome assembly of Antirrhinum majus, revealing evolutionary relationships and genetic architectures of complex traits.

Systematic Analysis Of RNA-Seq-Based Gene Co-Expression Across Multiple Plants

The paper constructs and analyzes RNA-seq-based gene co-expression networks in four plant species, identifying conserved and species-specific modules linked to biological functions and agronomic traits.

High-Quality Rice RNA-Seq-Based Co-Expression Network For Predicting Gene Function And Regulation

The paper builds a high-quality rice RNA-seq-based gene co-expression network using an ensemble method and demonstrates its utility for predicting gene function and regulation.