A novel method to identify pre-microRNA in various species knowledge base on various species [0.03%]
一种基于多种物种知识库识别前体微小RNAs的新方法
Tianyi Zhao,Ningyi Zhang,Ying Zhang et al.
Tianyi Zhao et al.
Background: More than 1/3 of human genes are regulated by microRNAs. The identification of microRNA (miRNA) is the precondition of discovering the regulatory mechanism of miRNA and developing the cure for genetic diseases...
Revealing protein functions based on relationships of interacting proteins and GO terms [0.03%]
基于相互作用蛋白和GO术语关系揭示蛋白质功能
Zhixia Teng,Maozu Guo,Xiaoyan Liu et al.
Zhixia Teng et al.
Background: In recent years, numerous computational methods predicted protein function based on the protein-protein interaction (PPI) network. These methods supposed that two proteins share the same function if they inter...
Constructing an integrated gene similarity network for the identification of disease genes [0.03%]
用于识别疾病基因的综合基因相似网络构建
Zhen Tian,Maozu Guo,Chunyu Wang et al.
Zhen Tian et al.
Background: Discovering novel genes that are involved human diseases is a challenging task in biomedical research. In recent years, several computational approaches have been proposed to prioritize candidate disease genes...
Investigations on factors influencing HPO-based semantic similarity calculation [0.03%]
基于HPO的语义相似性计算影响因素研究
Jiajie Peng,Qianqian Li,Xuequn Shang
Jiajie Peng
Background: Although disease diagnosis has greatly benefited from next generation sequencing technologies, it is still difficult to make the right diagnosis purely based on sequencing technologies for many diseases with c...
Dynamically analyzing cell interactions in biological environments using multiagent social learning framework [0.03%]
基于多主体社会学习框架的生物环境下的细胞相互作用的动态分析方法
Chengwei Zhang,Xiaohong Li,Shuxin Li et al.
Chengwei Zhang et al.
Background: Biological environment is uncertain and its dynamic is similar to the multiagent environment, thus the research results of the multiagent system area can provide valuable insights to the understanding of biolo...
Multiple kernels learning-based biological entity relationship extraction method [0.03%]
基于多核学习的生物实体关系抽取方法
Xu Dongliang,Pan Jingchang,Wang Bailing
Xu Dongliang
Background: Automatic extracting protein entity interaction information from biomedical literature can help to build protein relation network and design new drugs. There are more than 20 million literature abstracts inclu...
Miguel Ángel Rodríguez-García,Georgios V Gkoutos,Paul N Schofield et al.
Miguel Ángel Rodríguez-García et al.
Background: Integration and analysis of phenotype data from humans and model organisms is a key challenge in building our understanding of normal biology and pathophysiology. However, the range of phenotypes and anatomica...
Maryam Khordad,Robert E Mercer
Maryam Khordad
Background: One important type of information contained in biomedical research literature is the newly discovered relationships between phenotypes and genotypes. Because of the large quantity of literature, a reliable aut...
Experiences from the anatomy track in the ontology alignment evaluation initiative [0.03%]
语义网领域本体解剖及匹配评估计划的经验教训
Zlatan Dragisic,Valentina Ivanova,Huanyu Li et al.
Zlatan Dragisic et al.
Background: One of the longest running tracks in the Ontology Alignment Evaluation Initiative is the Anatomy track which focuses on aligning two anatomy ontologies. The Anatomy track was started in 2005. In 2005 and 2006 ...
Matching disease and phenotype ontologies in the ontology alignment evaluation initiative [0.03%]
疾病和表型本体匹配在本体对齐评测计划中的表现
Ian Harrow,Ernesto Jiménez-Ruiz,Andrea Splendiani et al.
Ian Harrow et al.
Background: The disease and phenotype track was designed to evaluate the relative performance of ontology matching systems that generate mappings between source ontologies. Disease and phenotype ontologies are important f...