National Center for Biomedical Ontology: advancing biomedicine through structured organization of scientific knowledge

The National Center for Biomedical Ontology is a consortium that comprises leading informaticians, biologists, clinicians, and ontologists, funded by the National Institutes of Health (NIH) Roadmap, to develop innovative technology and methods that allow scientists to record, manage, and disseminate biomedical information and knowledge in machine-processable form. The goals of the Center are (1) to help unify the divergent and isolated efforts in ontology development by promoting high quality open-source, standards-based tools to create, manage, and use ontologies, (2) to create new software tools so that scientists can use ontologies to annotate and analyze biomedical data, (3) to provide a national resource for the ongoing evaluation, integration, and evolution of biomedical ontologies and associated tools and theories in the context of driving biomedical projects (DBPs), and (4) to disseminate the tools and resources of the Center and to identify, evaluate, and communicate best practices of ontology development to the biomedical community. Through the research activities within the Center, collaborations with the DBPs, and interactions with the biomedical community, our goal is to help scientists to work more effectively in the e-science paradigm, enhancing experiment design, experiment execution, data analysis, information synthesis, hypothesis generation and testing, and understand human disease.     

一种基于本体集成的生物医学数据库中语义映射维护方法研究

生物医学数据库到生物医学本体的语义映射是基于本体集成生物医学数据库系统的一个重要环节。而生物医学本体随着学科的发展不断演化,造成了集成系统不稳定。针对这个问题,本文在本体演化条件下,分析并发现了语义映射的变化规律,设计了对应的维护流程和维护方法,并通过计算维护收益率证明了该方法对映射的维护是有效性的,从而增强了集成系统在本体演化条件下的稳定性。     

一种基于本体集成的生物医学数据库中语义映射维护方法研究

生物医学数据库到生物医学本体的语义映射是基于本体集成生物医学数据库系统的一个重要环节.而生物医学本体随着学科的发展不断演化,造成了集成系统不稳定.针对这个问题,本文在本体演化条件下,分析并发现了语义映射的变化规律,设计了对应的维护流程和维护方法,并通过计算维护收益率证明了该方法对映射的维护是有效性的,从而增强了集成系统在本体演化条件下的稳定性.     

Recent Advances in CRISPR‐Cas9 Genome Editing Technology for Biological and Biomedical Investigations

The Type II CRISPR‐Cas9 system is a simple, efficient, and versatile tool for targeted genome editing in a wide range of organisms and cell types. It continues to gain more scientific interest and has established itself as an extremely powerful technology within our synthetic biology toolkit. It works upon a targeted site and generates a double strand breaks that become repaired by either the NHEJ or the HDR pathway, modifying or permanently replacing the genomic target sequences of interest. These can include viral targets, single‐mutation genetic diseases, and multiple‐site corrections for wide scale disease states, offering the potential to manage and cure some of mankind's most persistent biomedical menaces. Here, we present the developing progress and future potential of CRISPR‐Cas9 in biological and biomedical investigations, toward numerous therapeutic, biomedical, and biotechnological applications, as well as some of the challenges within. J. Cell. Biochem. 119: 81–94, 2018. © 2017 Wiley Periodicals, Inc.     

The Neural/Immune Gene Ontology: clipping the Gene Ontology for neurological and immunological systems

Background  

The Gene Ontology (GO) is used to describe genes and gene products from many organisms. When used for functional annotation of microarray data, GO is often slimmed by editing so that only higher level terms remain. This practice is designed to improve the summarizing of experimental results by grouping high level terms and the statistical power of GO term enrichment analysis.     

长骨缺损修复用生物医学材料

本文根据天然骨的组织和结构特点,论述了长骨缺损对修复材料的要求,提出了通过材料复合,仿生设计和快速成型制备功能梯度长骨修复材料的方法。     

Measuring the Evolution of Ontology Complexity: The Gene Ontology Case Study

Ontologies support automatic sharing, combination and analysis of life sciences data. They undergo regular curation and enrichment. We studied the impact of an ontology evolution on its structural complexity. As a case study we used the sixty monthly releases between January 2008 and December 2012 of the Gene Ontology and its three independent branches, i.e. biological processes (BP), cellular components (CC) and molecular functions (MF). For each case, we measured complexity by computing metrics related to the size, the nodes connectivity and the hierarchical structure.The number of classes and relations increased monotonously for each branch, with different growth rates. BP and CC had similar connectivity, superior to that of MF. Connectivity increased monotonously for BP, decreased for CC and remained stable for MF, with a marked increase for the three branches in November and December 2012. Hierarchy-related measures showed that CC and MF had similar proportions of leaves, average depths and average heights. BP had a lower proportion of leaves, and a higher average depth and average height. For BP and MF, the late 2012 increase of connectivity resulted in an increase of the average depth and average height and a decrease of the proportion of leaves, indicating that a major enrichment effort of the intermediate-level hierarchy occurred.The variation of the number of classes and relations in an ontology does not provide enough information about the evolution of its complexity. However, connectivity and hierarchy-related metrics revealed different patterns of values as well as of evolution for the three branches of the Gene Ontology. CC was similar to BP in terms of connectivity, and similar to MF in terms of hierarchy. Overall, BP complexity increased, CC was refined with the addition of leaves providing a finer level of annotations but decreasing slightly its complexity, and MF complexity remained stable.     

Biomedical training for developing countries

A conference on this topic sponsored by the International Union of Biochemistry and Molecular Biology (IUBMB) was held in Oxford on 11 and 12 April 1995 under the auspices of the Oxford International Biomedical Centre (OIBC)     

Machine Learning for Biomedical Literature Triage

This paper presents a machine learning system for supporting the first task of the biological literature manual curation process, called triage. We compare the performance of various classification models, by experimenting with dataset sampling factors and a set of features, as well as three different machine learning algorithms (Naive Bayes, Support Vector Machine and Logistic Model Trees). The results show that the most fitting model to handle the imbalanced datasets of the triage classification task is obtained by using domain relevant features, an under-sampling technique, and the Logistic Model Trees algorithm.     

纳米材料在生物医学检测中的应用

纳米技术的兴起,对生物医学领域的变革产生了深远的影响。纳米材料是纳米技术发展的重要基础,它具有许多传统材料所不具备的独特的理化性质,因此在生物医学、传感器等重要技术领域有着广泛的应用前景。对几类常见的纳米材料包括纳米金、量子点、磁性纳米粒子、碳纳米管和硅纳米线在蛋白质、DNA、金属离子以及生物相关分子检测方面的应用进行综述。