A two-stage hybrid model for dissolved oxygen prediction and control in aquaculture - Ziang Chen; Huiting Hu; Shuangyin Liu; Zhuhong Che; Xinmiao Wang; Zhuhua Hu; Tonglai Liu; Meng Cui; Longqin Xu (Aquaculture International, 2025)

Report on the 4th Board Meeting of the International Human Phenome Consortium - Mei Tian; Han Liu; Shiwen Peng; Zhong Yang; Weishuo Tao; Huiting Che; Xuanxuan Gao; Li Jin (Phenomics, 2024)

Phenomic Imaging - Lizhen Lan; Kai Feng; Yudan Wu; Wenbo Zhang; Ling Wei; Huiting Che; Le Xue; Yidan Gao; Ji Tao; Shufang Qian; Wenzhao Cao; Jun Zhang; et al. (Phenomics, 2023)
Human phenomics is defined as the comprehensive collection of observable phenotypes and characteristics influenced by a complex interplay among factors at multiple scales. These factors include genes, epigenetics at the microscopic level, organs, microbiome at the mesoscopic level, and diet and environmental exposures at the macroscopic level. “Phenomic imaging” utilizes various imaging techniques to visualize and measure anatomical structures, biological functions, metabolic processes, and biochemical activities across different scales, both in vivo and ex vivo. Unlike conventional medical imaging focused on disease diagnosis, phenomic imaging captures both normal and abnormal traits, facilitating detailed correlations between macro- and micro-phenotypes. This approach plays a crucial role in deciphering phenomes. This review provides an overview of different phenomic imaging modalities and their applications in human phenomics. Additionally, it explores the associations between phenomic imaging and other omics disciplines, including genomics, transcriptomics, proteomics, immunomics, and metabolomics. By integrating phenomic imaging with other omics data, such as genomics and metabolomics, a comprehensive understanding of biological systems can be achieved. This integration paves the way for the development of new therapeutic approaches and diagnostic tools.

Report on the 3rd Board Meeting of the International Human Phenome Consortium - Mei Tian; Han Liu; Shunling Chen; Zhong Yang; Weishuo Tao; Shiwen Peng; Huiting Che; Li Jin (Phenomics, 2023)