据Karasilov M 2025年108日(Nature,2025 Oct 8.doi:10.1038/s41586-025-09603-w.)报道,瑞士苏黎世联邦理工学院等机构的研究人员开发出了一种名为MetaGraph的方法,让任何人都能像使用谷歌搜索一样,快速、准确、低成本地在全球基因数据...据Karasilov M 2025年108日(Nature,2025 Oct 8.doi:10.1038/s41586-025-09603-w.)报道,瑞士苏黎世联邦理工学院等机构的研究人员开发出了一种名为MetaGraph的方法,让任何人都能像使用谷歌搜索一样,快速、准确、低成本地在全球基因数据库中搜寻特定的DNA、RNA或蛋白质序列;这意味着一线医生或许能瞬间比对肿瘤患者的基因突变,环保科学家能快速监测水体中的抗生素耐药基因,研究人员能发现物种间未知的基因交流,这不仅是技术上的突破,更是推动精准医疗、公共卫生和基础科研民主化的关键一步。展开更多
Academic literature retrieval concerns about the selection of papers that are most likely to match a user's information needs. Most of the retrieval systems are limited to list-output models, in which the retrieva...Academic literature retrieval concerns about the selection of papers that are most likely to match a user's information needs. Most of the retrieval systems are limited to list-output models, in which the retrieval results are isolated from each other. In this paper, we aim to uncover the relationships between the retrieval results and propose a method to build structural retrieval results for academic literature, which we call a paper evolution graph(PEG).The PEG describes the evolution of diverse aspects of input queries through several evolution chains of papers. By using the author, citation, and content information, PEGs can uncover various underlying relationships among the papers and present the evolution of articles from multiple viewpoints. Our system supports three types of input queries: keyword query, single-paper query, and two-paper query. The construction of a PEG consists mainly of three steps. First, the papers are soft-clustered into communities via metagraph factorization, during which the topic distribution of each paper is obtained. Second, topically cohesive evolution chains are extracted from the communities that are relevant to the query. Each chain focuses on one aspect of the query. Finally, the extracted chains are combined to generate a PEG, which fully covers all the topics of the query. Experimental results on a real-world dataset demonstrate that the proposed method can construct meaningful PEGs.展开更多
文摘据Karasilov M 2025年108日(Nature,2025 Oct 8.doi:10.1038/s41586-025-09603-w.)报道,瑞士苏黎世联邦理工学院等机构的研究人员开发出了一种名为MetaGraph的方法,让任何人都能像使用谷歌搜索一样,快速、准确、低成本地在全球基因数据库中搜寻特定的DNA、RNA或蛋白质序列;这意味着一线医生或许能瞬间比对肿瘤患者的基因突变,环保科学家能快速监测水体中的抗生素耐药基因,研究人员能发现物种间未知的基因交流,这不仅是技术上的突破,更是推动精准医疗、公共卫生和基础科研民主化的关键一步。
基金Project supported by the National Key R&D Program of China(No.2018YFB0505000)the National Natural Science Foundation of China(No.61571393)
文摘Academic literature retrieval concerns about the selection of papers that are most likely to match a user's information needs. Most of the retrieval systems are limited to list-output models, in which the retrieval results are isolated from each other. In this paper, we aim to uncover the relationships between the retrieval results and propose a method to build structural retrieval results for academic literature, which we call a paper evolution graph(PEG).The PEG describes the evolution of diverse aspects of input queries through several evolution chains of papers. By using the author, citation, and content information, PEGs can uncover various underlying relationships among the papers and present the evolution of articles from multiple viewpoints. Our system supports three types of input queries: keyword query, single-paper query, and two-paper query. The construction of a PEG consists mainly of three steps. First, the papers are soft-clustered into communities via metagraph factorization, during which the topic distribution of each paper is obtained. Second, topically cohesive evolution chains are extracted from the communities that are relevant to the query. Each chain focuses on one aspect of the query. Finally, the extracted chains are combined to generate a PEG, which fully covers all the topics of the query. Experimental results on a real-world dataset demonstrate that the proposed method can construct meaningful PEGs.
