Nanoinformatics knowledge infrastructures: bringing efficient information management to nanomedical research

Nanotechnology represents an area of particular promise and significant opportunity across multiple scientific disciplines. Ongoing nanotechnology research ranges from the characterization of nanoparticles and nanomaterials to the analysis and processing of experimental data seeking correlations between nanoparticles and their functionalities and side effects. Due to their special properties, nanoparticles are suitable for cellular-level diagnostics and therapy, offering numerous applications in medicine, e.g. development of biomedical devices, tissue repair, drug delivery systems and biosensors. In nanomedicine, recent studies are producing large amounts of structural and property data, highlighting the role for computational approaches in information management. While in vitro and in vivo assays are expensive, the cost of computing is falling. Furthermore, improvements in the accuracy of computational methods (e.g. data mining, knowledge discovery, modeling and simulation) have enabled effective tools to automate the extraction, management and storage of these vast data volumes. Since this information is widely distributed, one major issue is how to locate and access data where it resides (which also poses data-sharing limitations). The novel discipline of nanoinformatics addresses the information challenges related to nanotechnology research. In this paper, we summarize the needs and challenges in the field and present an overview of extant initiatives and efforts

Ontology-Based Search Procedure to Identify Tissue Samples in an Autopsy Archive: A Pilot Study

This study aimed to obtain a detailed record of all autopsy specimens analyzed in the Pathology Department of the Emergency Clinical Hospital for Children, Cluj-Napoca from 1974 to 2018, by using an ontology-based search procedure (OSP) intended to identify the paraffin-embedded stored specimens in pathology reports. Two thousand nine hundred and fifty-six autopsy reports were analyzed using a list of histology terms and expressions commonly found in the microscopic descriptions of the autopsy reports, in Romanian. One pathologist was asked to evaluate the microscopic descriptive part of the autopsy reports for 300 cases and to classify the identified histology specimens according to the ICD-topography codes. The results were then compared with the OSP results. The validation assay returned a 97.32% sensitivity and a 99.48% specificity of the applied ontology-based search procedure when taking as a reference the assessment performed by a pathologist. The most common specimens identified were in the categories of the lower respiratory system (lung, trachea), liver, biliary tract, pancreas and urinary system. The proposed ontology can link valuable information to a highly reliable pathology-based autopsy registry allowing researchers to gain access to specimens stored in the pathology archives, and to facilitate disease registration, data extraction and reporting. This procedure represents a good starting point for developing suitable solutions to be implemented in registries, data banks and for the development of ontology-based registration tools

Methods to Facilitate the Capture, Use, and Reuse of Structured and Unstructured Clinical Data.

Electronic health records (EHRs) have great potential to improve quality of care and to support clinical and translational research. While EHRs are being increasingly implemented in U.S. hospitals and clinics, their anticipated benefits have been largely unachieved or underachieved. Among many factors, tedious documentation requirements and the lack of effective information retrieval tools to access and reuse data are two key reasons accounting for this deficiency. In this dissertation, I describe my research on developing novel methods to facilitate the capture, use, and reuse of both structured and unstructured clinical data. Specifically, I develop a framework to investigate potential issues in this research topic, with a focus on three significant challenges. The first challenge is structured data entry (SDE), which can be facilitated by four effective strategies based on my systematic review. I further propose a multi-strategy model to guide the development of future SDE applications. In the follow-up study, I focus on workflow integration and evaluate the feasibility of using EHR audit trail logs for clinical workflow analysis. The second challenge is the use of clinical narratives, which can be supported by my innovative information retrieval (IR) technique called “semantically-based query recommendation (SBQR)”. My user experiment shows that SBQR can help improve the perceived performance of a medical IR system, and may work better on search tasks with average difficulty. The third challenge involves reusing EHR data as a reference standard to benchmark the quality of other health-related information. My study assesses the readability of trial descriptions on ClinicalTrials.gov and found that trial descriptions are very hard to read, even harder than clinical notes. My dissertation has several contributions. First, it conducts pioneer studies with innovative methods to improve the capture, use, and reuse of clinical data. Second, my dissertation provides successful examples for investigators who would like to conduct interdisciplinary research in the field of health informatics. Third, the framework of my research can be a great tool to generate future research agenda in clinical documentation and EHRs. I will continue exploring innovative and effective methods to maximize the value of EHRs.PHDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/135845/1/tzuyu_1.pd

Cohort Identification Using Semantic Web Technologies: Ontologies and Triplestores as Engines for Complex Computable Phenotyping

Electronic health record (EHR)-based computable phenotypes are algorithms used to identify individuals or populations with clinical conditions or events of interest within a clinical data repository. Due to a lack of EHR data standardization, computable phenotypes can be semantically ambiguous and difficult to share across institutions. In this research, I propose a new computable phenotyping methodological framework based on semantic web technologies, specifically ontologies, the Resource Description Framework (RDF) data format, triplestores, and Web Ontology Language (OWL) reasoning. My hypothesis is that storing and analyzing clinical data using these technologies can begin to address the critical issues of semantic ambiguity and lack of interoperability in the context of computable phenotyping. To test this hypothesis, I compared the performance of two variants of two computable phenotypes (for depression and rheumatoid arthritis, respectively). The first variant of each phenotype used a list of ICD-10-CM codes to define the condition; the second variant used ontology concepts from SNOMED and the Human Phenotype Ontology (HPO). After executing each variant of each phenotype against a clinical data repository, I compared the patients matched in each case to see where the different variants overlapped and diverged. Both the ontologies and the clinical data were stored in an RDF triplestore to allow me to assess the interoperability advantages of the RDF format for clinical data. All tested methods successfully identified cohorts in the data store, with differing rates of overlap and divergence between variants. Depending on the phenotyping use case, SNOMED and HPO’s ability to more broadly define many conditions due to complex relationships between their concepts may be seen as an advantage or a disadvantage. I also found that RDF triplestores do indeed provide interoperability advantages, despite being far less commonly used in clinical data applications than relational databases. Despite the fact that these methods and technologies are not “one-size-fits-all,” the experimental results are encouraging enough for them to (1) be put into practice in combination with existing phenotyping methods or (2) be used on their own for particularly well-suited use cases.Doctor of Philosoph

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe

Dissemination and visualisation of biological data

With the recent advent of various waves of technological advances, the amount of biological data being generated has exploded. As a consequence of this data deluge, new challenges have emerged in the field of biological data management. In order to maximize the knowledge extracted from the huge amount of biological data produced it is of great importance for the research community that data dissemination and visualisation challenges are tackled. Opening and sharing our data and working collaboratively will benefit the scientific community as a whole and to move towards that end, new developements, tools and techniques are needed. Nowadays, many small research groups are capable of producing important and interesting datasets. The release of those datasets can greatly increase their scientific value. In addition, the development of new data analysis algorithms greatly benefits from the availability of a big corpus of annotated datasets for training and testing purposes, giving new and better algorithms to biomedical sciences in return. None of these would be feasible without large amounts of biological data made freely and publicly available. Dissemination The Distributed Annotation System (DAS) is a protocol designed to publish and integrate annotations on biological entities in a distributed way. DAS is structured as a client-server system where the client retrieves data from one or more servers and to further process and visualise. Nowadays, setting up a DAS server imposes some requirements not met by many research groups. With the aim of removing the hassle of setting up a DAS server, a new software platform has been developed: easyDAS. easyDAS is a hosted platform to automatically create DAS servers. Using a simple web interface the user can upload a data file, describe its contents and a new DAS server will be automatically created and data will be publicly available to DAS clients. Visualisation One of the most broadly used visualization paradigms for genomic data are genomic browsers. A genomic browser is capable of displaying different sets of features positioned relative to a sequence. It is possible to explore the sequence and the features by moving around and zooming in and out. When this project was started, in 2007, all major genome browsers offered quite an static experience. It was possible to browse and explore data, but is was done through a set of buttons to the genome a certain amount of bases to left or right or zooming in and out. From an architectural point of view, all web-based genome browsers were very similar: they all had a relatively thin clien-side part in charge of showing images and big backend servers taking care of everything else. Every change in the display parameters made by the user triggered a request to the server, impacting the perceived responsiveness. We created a new prototype genome browser called GenExp, an interactive web-based browser with canvas based client side data rendering. It offers fluid direct interaction with the genome representation and it's possible to use the mouse drag it and use the mouse wheel to change the zoom level. GenExp offers also some quite unique features, such as its multi-window capabilities that allow a user to create an arbitrary number of independent or linked genome windows and its ability to save and share browsing sessions. GenExp is a DAS client and all data is retrieved from DAS sources. It is possible to add any available DAS data source including all data in Ensembl, UCSC and even the custom ones created with easyDAS. In addition, we developed a javascript DAS client library, jsDAS. jsDAS is a complete DAS client library that will take care of everything DAS related in a javascript application. jsDAS is javascript library agnostic and can be used to add DAS capabilities to any web application. All software developed in this thesis is freely available under an open source license.Les recents millores tecnològiques han portat a una explosió en la quantitat de dades biològiques que es generen i a l'aparició de nous reptes en el camp de la gestió de les dades biològiques. Per a maximitzar el coneixement que podem extreure d'aquestes ingents quantitats de dades cal que solucionem el problemes associats al seu anàlisis, i en particular a la seva disseminació i visualització. La compartició d'aquestes dades de manera lliure i gratuïta pot beneficiar en gran mesura a la comunitat científica i a la societat en general, però per a fer-ho calen noves eines i tècniques. Actualment, molts grups són capaços de generar grans conjunts de dades i la seva publicació en pot incrementar molt el valor científic. A més, la disponibilitat de grans conjunts de dades és necessària per al desenvolupament de nous algorismes d'anàlisis. És important, doncs, que les dades biològiques que es generen siguin accessibles de manera senzilla, estandaritzada i lliure. Disseminació El Sistema d'Anotació Distribuïda (DAS) és un protocol dissenyat per a la publicació i integració d'anotacions sobre entitats biològiques de manera distribuïda. DAS segueix una esquema de client-servidor, on el client obté dades d'un o més servidors per a combinar-les, processar-les o visualitzar-les. Avui dia, però, crear un servidor DAS necessita uns coneixements i infraestructures que van més enllà dels recursos de molts grups de recerca. Per això, hem creat easyDAS, una plataforma per a la creació automàtica de servidors DAS. Amb easyDAS un usuari pot crear un servidor DAS a través d'una senzilla interfície web i amb només alguns clics. Visualització Els navegadors genomics són un dels paradigmes de de visualització de dades genòmiques més usats i permet veure conjunts de dades posicionades al llarg d'una seqüència. Movent-se al llarg d'aquesta seqüència és possibles explorar aquestes dades. Quan aquest projecte va començar, l'any 2007, tots els grans navegadors genomics oferien una interactivitat limitada basada en l'ús de botons. Des d'un punt de vista d'arquitectura tots els navegadors basats en web eren molt semblants: un client senzill encarregat d'ensenyar les imatges i un servidor complex encarregat d'obtenir les dades, processar-les i generar les imatges. Així, cada canvi en els paràmetres de visualització requeria una nova petició al servidor, impactant molt negativament en la velocitat de resposta percebuda. Vam crear un prototip de navegador genòmic anomenat GenExp. És un navegador interactiu basat en web que fa servir canvas per a dibuixar en client i que ofereix la possibilitatd e manipulació directa de la respresentació del genoma. GenExp té a més algunes característiques úniques com la possibilitat de crear multiples finestres de visualització o la possibilitat de guardar i compartir sessions de navegació. A més, com que és un client DAS pot integrar les dades de qualsevol servidor DAS com els d'Ensembl, UCSC o fins i tot aquells creats amb easyDAS. A més, hem desenvolupat jsDAS, la primera llibreria de client DAS completa escrita en javascript. jsDAS es pot integrar en qualsevol aplicació DAS per a dotar-la de la possibilitat d'accedir a dades de servidors DAS. Tot el programari desenvolupat en el marc d'aquesta tesis està lliurement disponible i sota una llicència de codi lliure