Knowledge management for systems biology a general and visually driven framework applied to translational medicine

Background: To enhance our understanding of complex biological systems like diseases we need to put all of the available data into context and use this to detect relations, pattern and rules which allow predictive hypotheses to be defined. Life science has become a data rich science with information about the behaviour of millions of entities like genes, chemical compounds, diseases, cell types and organs, which are organised in many different databases and/or spread throughout the literature. Existing knowledge such as genotype - phenotype relations or signal transduction pathways must be semantically integrated and dynamically organised into structured networks that are connected with clinical and experimental data. Different approaches to this challenge exist but so far none has proven entirely satisfactory. Results: To address this challenge we previously developed a generic knowledge management framework, BioXM , which allows the dynamic, graphic generation of domain specific knowledge representation models based on specific objects and their relations supporting annotations and ontologies. Here we demonstrate the utility of BioXM for knowledge management in systems biology as part of the EU FP6 BioBridge project on translational approaches to chronic diseases. From clinical and experimental data, text-mining results and public databases we generate a chronic obstructive pulmonary disease (COPD) knowledge base and demonstrate its use by mining specific molecular networks together with integrated clinical and experimental data. Conclusions: We generate the first semantically integrated COPD specific public knowledge base and find that for the integration of clinical and experimental data with pre-existing knowledge the configuration based set-up enabled by BioXM reduced implementation time and effort for the knowledge base compared to similar systems implemented as classical software development projects. The knowledgebase enables the retrieval of sub-networks including protein-protein interaction, pathway, gene - disease and gene - compound data which are used for subsequent data analysis, modelling and simulation. Pre-structured queries and reports enhance usability; establishing their use in everyday clinical settings requires further simplification with a browser based interface which is currently under development

生醫分析系統之語意整合

[[abstract]]這計畫提議建立一個知識系統，允許生物醫學的研究人員透過以自然語言查詢方 式，綜合查詢複雜的生物資訊數據及影像訊息。我們的數據庫的目標是使數據的輸 入更有效率的，更有組織性，容易取回，及使操作和綜合變得容易。此系統以阿茲海 默症作為研究的對象。這一個知識系統與傳統知識系統的基本的區別在於它支援複雜 的數據組織和一個強大的查詢界面。 SemanticObjects 是由美國加州大學Irvine 分校和日本NEC 共同開發的一個物件 相關的平台，目的是為建造一物件知識系統。它允許使用者有效的組織及儲存生物學 模式和數據成階層式的複雜物件。使用者可利用結構性的自然語言來查詢及利用此知 識系統的數據。 最後，我們將迅速地把這個以SemanticObjects 為主的知識系統成為網站應用。這 使其它的研究人員可分享及獲得是項研究的結果。 我們提議的系統由以下的數個模組組成，a) 文字採礦模組，b) microarry/SNP 模 組，c) 基因網路模組，d)影像模組和e)實驗模組。 This proposal suggests building a knowledge system that allows biomedical researchers to synthesize complex bioinformatics information and images data via natural language query. The goal of our database is to facilitate efficient data entry, organization, retrieval, manipulation and integration. The Alzheimer』s Disease was chosen as our study case. A fundamental distinction of the biological database addressed in this research and the others is that it supports both complex data organization and a powerful querying facility. SemanticObjects is an object-relational platform that has been jointly developed by University of California, Irvine and NEC Soft, Japan as a tool for building object knowledge systems. It allows users to efficiently organize and store biological models and data as complex objects that are hierarchically structured. User can query and manipulate the data in Structured Natural Language (SNL). Finally, we will rapidly deploy this SemanticObjects database into a web application. This makes it easy for the research community to share the results obtained from proposed research. Our proposed system consists of: a) a text mining module, b) a microarry/SNP module, c) a gene network module, d) an image module, and e) a web laboratory module

A common type system for clinical natural language processing

Background: One challenge in reusing clinical data stored in electronic medical records is that these data are heterogenous. Clinical Natural Language Processing (NLP) plays an important role in transforming information in clinical text to a standard representation that is comparable and interoperable. Information may be processed and shared when a type system specifies the allowable data structures. Therefore, we aim to define a common type system for clinical NLP that enables interoperability between structured and unstructured data generated in different clinical settings. Results: We describe a common type system for clinical NLP that has an end target of deep semantics based on Clinical Element Models (CEMs), thus interoperating with structured data and accommodating diverse NLP approaches. The type system has been implemented in UIMA (Unstructured Information Management Architecture) and is fully functional in a popular open-source clinical NLP system, cTAKES (clinical Text Analysis and Knowledge Extraction System) versions 2.0 and later. Conclusions: We have created a type system that targets deep semantics, thereby allowing for NLP systems to encapsulate knowledge from text and share it alongside heterogenous clinical data sources. Rather than surface semantics that are typically the end product of NLP algorithms, CEM-based semantics explicitly build in deep clinical semantics as the point of interoperability with more structured data types

Reviving the parameter revolution in semantics

Montague and Kaplan began a revolution in semantics, which promised to explain how a univocal expression could make distinct truth-conditional contributions in its various occurrences. The idea was to treat context as a parameter at which a sentence is semantically evaluated. But the revolution has stalled. One salient problem comes from recurring demonstratives: "He is tall and he is not tall". For the sentence to be true at a context, each occurrence of the demonstrative must make a different truth-conditional contribution. But this difference cannot be accounted for by standard parameter sensitivity. Semanticists, consoled by the thought that this ambiguity would ultimately be needed anyhow to explain anaphora, have been too content to posit massive ambiguities in demonstrative pronouns. This article aims to revived the parameter revolution by showing how to treat demonstrative pronouns as univocal while providing an account of anaphora that doesn't end up re-introducing the ambiguity

A Database Approach for Modeling and Querying Video Data

Indexing video data is essential for providing content based access. In this paper, we consider how database technology can offer an integrated framework for modeling and querying video data. As many concerns in video (e.g., modeling and querying) are also found in databases, databases provide an interesting angle to attack many of the problems. From a video applications perspective, database systems provide a nice basis for future video systems. More generally, database research will provide solutions to many video issues even if these are partial or fragmented. From a database perspective, video applications provide beautiful challenges. Next generation database systems will need to provide support for multimedia data (e.g., image, video, audio). These data types require new techniques for their management (i.e., storing, modeling, querying, etc.). Hence new solutions are significant. This paper develops a data model and a rule-based query language for video content based indexing and retrieval. The data model is designed around the object and constraint paradigms. A video sequence is split into a set of fragments. Each fragment can be analyzed to extract the information (symbolic descriptions) of interest that can be put into a database. This database can then be searched to find information of interest. Two types of information are considered: (1) the entities (objects) of interest in the domain of a video sequence, (2) video frames which contain these entities. To represent these information, our data model allows facts as well as objects and constraints. We present a declarative, rule-based, constraint query language that can be used to infer relationships about information represented in the model. The language has a clear declarative and operational semantics. This work is a major revision and a consolidation of [12, 13].This is an extended version of the article in: 15th International Conference on Data Engineering, Sydney, Australia, 1999