Dataset Information

The Translational Medicine Ontology and Knowledge Base: driving personalized medicine by bridging the gap between bench and bedside.

ABSTRACT: Translational medicine requires the integration of knowledge using heterogeneous data from health care to the life sciences. Here, we describe a collaborative effort to produce a prototype Translational Medicine Knowledge Base (TMKB) capable of answering questions relating to clinical practice and pharmaceutical drug discovery.We developed the Translational Medicine Ontology (TMO) as a unifying ontology to integrate chemical, genomic and proteomic data with disease, treatment, and electronic health records. We demonstrate the use of Semantic Web technologies in the integration of patient and biomedical data, and reveal how such a knowledge base can aid physicians in providing tailored patient care and facilitate the recruitment of patients into active clinical trials. Thus, patients, physicians and researchers may explore the knowledge base to better understand therapeutic options, efficacy, and mechanisms of action.This work takes an important step in using Semantic Web technologies to facilitate integration of relevant, distributed, external sources and progress towards a computational platform to support personalized medicine.TMO can be downloaded from http://code.google.com/p/translationalmedicineontology and TMKB can be accessed at http://tm.semanticscience.org/sparql.

SUBMITTER: Luciano JS

PROVIDER: S-EPMC3102889 | biostudies-other | 2011 May

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

The Translational Medicine Ontology and Knowledge Base: driving personalized medicine by bridging the gap between bench and bedside.

Luciano Joanne S JS Andersson Bosse B Batchelor Colin C Bodenreider Olivier O Clark Tim T Denney Christine K CK Domarew Christopher C Gambet Thomas T Harland Lee L Jentzsch Anja A Kashyap Vipul V Kos Peter P Kozlovsky Julia J Lebo Timothy T Marshall Scott M SM McCusker Jamie P. JP McGuinness Deborah L DL Ogbuji Chimezie C Pichler Elgar E Powers Robert L RL Prud'hommeaux Eric E Samwald Matthias M Schriml Lynn L Tonellato Peter J PJ Whetzel Patricia L PL Zhao Jun J Stephens Susie S Dumontier Michel M

Journal of biomedical semantics 20110517

<h4>Background</h4>Translational medicine requires the integration of knowledge using heterogeneous data from health care to the life sciences. Here, we describe a collaborative effort to produce a prototype Translational Medicine Knowledge Base (TMKB) capable of answering questions relating to clinical practice and pharmaceutical drug discovery.<h4>Results</h4>We developed the Translational Medicine Ontology (TMO) as a unifying ontology to integrate chemical, genomic and proteomic data with dis ...[more]

PMID: 21624155

Similar Datasets

Project description:Natural language processing (NLP) techniques are increasingly being used in biology to automate the capture of new biological discoveries in text, which are being reported at a rapid rate. Yet, information represented in NLP data structures is classically very different from information organized with ontologies as found in model organisms or genetic databases. To facilitate the computational reuse and integration of information buried in unstructured text with that of genetic databases, we propose and evaluate a translational schema that represents a comprehensive set of phenotypic and genetic entities, as well as their closely related biomedical entities and relations as expressed in natural language. In addition, the schema connects different scales of biological information, and provides mappings from the textual information to existing ontologies, which are essential in biology for integration, organization, dissemination and knowledge management of heterogeneous phenotypic information. A common comprehensive representation for otherwise heterogeneous phenotypic and genetic datasets, such as the one proposed, is critical for advancing systems biology because it enables acquisition and reuse of unprecedented volumes of diverse types of knowledge and information from text.A novel representational schema, PGschema, was developed that enables translation of phenotypic, genetic and their closely related information found in textual narratives to a well-defined data structure comprising phenotypic and genetic concepts from established ontologies along with modifiers and relationships. Evaluation for coverage of a selected set of entities showed that 90% of the information could be represented (95% confidence interval: 86-93%; n = 268). Moreover, PGschema can be expressed automatically in an XML format using natural language techniques to process the text. To our knowledge, we are providing the first evaluation of a translational schema for NLP that contains declarative knowledge about genes and their associated biomedical data (e.g. phenotypes).http://zellig.cpmc.columbia.edu/PGschema

Project description:BackgroundNeuropsychiatric fluctuations (NpsyF) are frequent and disabling in people with Parkinson's disease (PD). In OFF-medication, NpsyF entail minus neuropsychiatric symptoms (NPS) like anxiety, apathy, sadness, and fatigue. In ON-medication, NpsyF consist in plus NPS, such as high mood, hypomania, and hyperactivity. Accurate identification of these NpsyF is essential to optimize the overall PD management. Due to lack of punctual scales, the neuropsychiatric fluctuation scale (NFS) has been recently designed to assess NpsyF in real time. The NFS comprises 20 items with two subscores for plus and minus NPS, and a total score.ObjectiveTo evaluate the psychometric properties of the NFS in PD.MethodsPD patients with motor fluctuations and healthy controls (HC) were assessed. In PD patients, the NFS was administrated in both the ON-and OFF-medication conditions, together with the movement disorders society-unified Parkinson disease rating scale parts I-IV. Depression (Beck depression scale II), apathy (Starkstein apathy scale) and non-motor fluctuations items of the Ardouin scale of behaviour in PD (ASBPD OFF and ON items) were also assessed. NFS internal structure was evaluated with principal component analysis consistency (PCA) in both medication conditions in PD patients and before emotional induction in HC. NFS internal consistency was assessed using Cronbach's alpha coefficient. NFS convergent and divergent validity was measured through correlations with BDI-II, Starktein, and ASBPD OFF and ON non motor items. Specificity was assessed comparing NFS global score between the HC and PD populations. Sensitivity was evaluated with t-student test comparing the ON-and the OFF-medication conditions for NFS global score and for minus and plus subscores.ResultsIn total, 101 consecutive PD patients and 181 HC were included. In PD patients and HC, PCA highlighted one component that explained 32-35 and 42% of the variance, respectively. Internal consistency was good for both the NFS-plus (alpha =0.88) and NFS-minus items (alpha =0.8). The NFS showed a good specifity for PD (p < 0.0001) and a good sensitivity to the medication condition (p < 0.0001).ConclusionThe satisfactory properties of the NFS support its use to assess acute neuropsychiatric fluctuations in PD patients, adding to available tools.

Dataset Information

The Translational Medicine Ontology and Knowledge Base: driving personalized medicine by bridging the gap between bench and bedside.

Publications

The Translational Medicine Ontology and Knowledge Base: driving personalized medicine by bridging the gap between bench and bedside.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets