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Congrats to Yufeu et al who won a “Guochuang” funding in the support of their 2.1k rice trait corpus research. “Guochuang”, pinyin of “国创”, is a nation-level research funding for supporting college students’ early science training. Check the link for more detail about their project: https://hzaubionlp.com/gene-to-trait-rice/

Thank Dr. Anastasios Nentidis from, Institute of Informatics and Telecommunications, NCSR Demokritos, for bringing an interesting MeSH semantic indexing talk. Report page: http://coi.hzau.edu.cn/info/1023/7772.htm

Thank Dr. George Paliouras from Institute of Informatics and Telecommunications, NCSR Demokritos for the interesting BioASQ talk. Report page: http://coi.hzau.edu.cn/info/1023/7717.htm

Let us welcome Xingyu Zhou to “upgrade” his lab role as an Research Assistant since today.

We are happy to release a video which introduces AGAC corpus and its application in mutation type discovery. A recent case study in gene-disease association discovery in AD is as well introduced. https://youtu.be/YlzxtMkaYQk Narrator: Yuxing Wang Alternatively, visit the video via this link. To visit the whole AGAC page, please click here.

Here, enrRiceTrait is a new tool for rice trait ontology. To present the enrichment, gene-trait association results are based on text mining and available data bases, while the traits are normalized by RTO 1.0, a newly developed rice trait ontology. https://www.youtube.com/watch?v=Fqjibzn7IxY Narrator: Yun Liu (Video released in Apr, 2021). Alternatively, watch the video here. ToContinueContinue reading “The lab is releasing a new video about enrRiceTrait”

We are happy to have Dr. Anastasia Krithara from National Center for Scientific Research “Demokritos” virtually visited us and give a wonderful online talk, entitled as “iASiS Open Data Graph: Automated Semantic Integration of Disease-Specific Knowledge”. http://coi.hzau.edu.cn/info/1023/7518.htm

A hill called Tuan Shan 团山, which is close to the East lake.

This work shows how AGAC serves for LOF/GOF mutation retrieval and Gene–Disease Association prediction. Link to the paper. Abstract Bridging heterogeneous mutation data fills in the gap between various data categories and propels discovery of disease-related genes. It is known that genome-wide association study (GWAS) infers significant mutation associations that link genotype and phenotype. However, due toContinueContinue reading ““AGAC on mutation information bridging” is published online. Congrats!”