评测一: 面向“基因-疾病”的关联语义挖掘任务 Task1: Text mining task for “Gene-Disease” association semantics, GDAS track, CHIP2022 想在无聊的暑假,顺手发表一篇高大尚的CHIP的论文吗? 想与各大顶尖NLP团队同台竞技吗? 想从这些队伍当中脱颖而出吗? 来吧~加入我们的比赛 风里雨里,我们在比赛中等你来 心动不如行动,在CHIP2022展现你风采 点击图片,扫左下方二维码,获取更多信息。 链接地址: CHIP官网链接:http://www.cips-chip.org.cn/2022/eval1 课题组评测任务页面链接:http://lit-evi.hzau.edu.cn/AGAC-CHIP2022/#about-section
Author Archives: hzaubionlp
Thank Dr. Jake Lever for giving the instructive talk
It is our great pleasure to invite Dr. Jake Lever for an online talk, <Text mining to assist biocuration for precision medicine>. Jake Lever博士研究重点是生物医学文本文本挖掘,利用信息提取方法来建立知识数据库并协助研究者找到正确的文献支持。近年来,Jake Lever博士在针对疾病自动化文本知识抽取上做出一系列有启发性的工作,其中发表在Nature Method上的CancerMine算法,精准挖掘癌症相关的 oncogene,tumor suppressor 和 driver gene,该工作受到较广泛关注。Jake Lever博士本次的报告围绕如下内容展开:当今生物医药领域的研究跨学科的特点日益明显,并且文献数量快速增长,这使得生物医药领域的研究人员在海量文献阅读过程中有效、快速、准确地定位知识带来巨大的挑战。因此我们必须建立自动化的方法来协助研究人员处理这些海量的知识,并引导他们走向新的方向。而信息提取方法提供了一个机会,去智能总结隐藏在海量文献中的综合生物医学知识。本次报告会讨论自然语言处理在生物医学应用中一些独特的挑战。 本次报告分为“Finding knowledge for biocurators”“Relation extraction at PubMed scale”“Categorizing coronavirus research”3部分,Jake Lever博士使用图表与动画结合,深入浅出地介绍了文本挖掘辅助精准医学的生物治疗过程,会后,参会师生就分享内容提出一系列问题,如“怎么样更可靠地挑选在多种癌症中起到关键作用的基因?”“文本通常是描述群体的,文本挖掘方法真的可以辅助精准医疗吗?”“机器学习更容易平衡P值和召回,深度学习如何才能做到这一点?”等问题,Jake博士针对大家的问题展开一系列解答,大家进行友好交流,与会者表示收获颇丰。
《AD分子标记物文本挖掘》-GenMed2014论文评述
《Linking hypothetical knowledge patterns to disease molecular signatures for biomarker discovery in Alzheimer’s disease》论文评述 文章《Linking hypothetical knowledge patterns to disease molecular signatures for biomarker discovery in Alzheimer’s disease》https://linkspringer.53yu.com/article/10.1186/s13073-014-0097-z 作者:Ashutosh Malhotra, Erfan Younesi, Shweta Bagewadi, Martin Hofmann-Apitius 引用方式:Malhotra, A., Younesi, E., Bagewadi, S., & Hofmann-Apitius, M. (2014). Linking hypothetical knowledge patterns to disease molecular signatures for biomarkerer discovery in Alzheimer’s disease. Genome medicine, 6(11), 1-11.Continue reading “《AD分子标记物文本挖掘》-GenMed2014论文评述”
请问在研讨中如何快速圈粉?
无它,但“手书”尔。
翻山志高远
古诗有云:“翻山志高远,拍鱼意盎然。”
《CancerVar》论文评述
文章《CancerVar: An artificial intelligence–empowered platform for clinical interpretation of somatic mutations in cancer》https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9075800/ 作者:Quan Li, Zilin Ren, Kajia Cao, Marilyn M. Li, Kai Wang, Yunyun Zhou 引用方式:Li, Q., Ren, Z., Cao, K., Li, M. M., Wang, K., & Zhou, Y. (2022). CancerVar: An artificial intelligence-empowered platform for clinical interpretation of somatic mutations in cancer. ScienceContinue reading “《CancerVar》论文评述”
《CancerMine》论文评述
文章《CancerMine: a literature-mined resource for drivers, oncogenes and tumor suppressors in cancer》https://www.nature.com/articles/s41592-019-0422-y#MOESM1 作者:Jake Lever , Eric Y. Zhao , Jasleen Grewal, Martin R. Jones and Steven J. M. Jones 引用方式:Lever, J. , Zhao, E. Y. , Grewal, J. , Jones, M. R. , & Jones, S. . (2019). Cancermine: a literature-mined resource for drivers, oncogenes and tumor suppressors in cancer.Continue reading “《CancerMine》论文评述”
《生物文本挖掘》课程结束了
这个学期的《生物文本挖掘与知识发现概论》课程结束了,课程论文册集结出来,舒了口气。Hope you would feel like the course.
Welcome you, new lab members.
Welcome you, Zhihan He, Qi Xiong, and Ziming Tang.
Our matrix/tensor joint decomposition model is published in JBI
We published the JDHMT model in JBI. https://www.sciencedirect.com/science/article/pii/S1532046421003026 Highlights(论文亮点) We propose a typical knowledge form in multi-relational heterogeneous graph, i.e., (gene/disease, uni-relation, other heterogeneous entity), (gene, multi-relation, disease), where a relatively sparse multi-relation knowledge between gene and disease is preserved. (我们在多关系异构图中提出了一种典型的知识形式,即(基因/疾病,单关系,其他异构实体),(基因,多关系,疾病),其中基因之间相对稀疏的多关系知识和 疾病得以保存。) 2. We curate human genes and diseases in seven mainstream datasets and construct a massiveContinue reading “Our matrix/tensor joint decomposition model is published in JBI”