Umit Topaloglu

Developing a semantically rich ontology for the biobank-administration domain

BackgroundBiobanks are a critical resource for translational science. Recently, semantic web technologies such as ontologies have been found useful in retrieving research data from biobanks. However, recent research has also shown that there is a lack of data about the administrative aspects of biobanks. These data would be helpful to answer research-relevant questions such as what is the scope of specimens collected in a biobank, what is the curation status of the specimens, and what is the contact information for curators of biobanks. Our use cases include giving researchers the ability to retrieve key administrative data (e.g. contact information, contacts affiliation, etc.) about the biobanks where specific specimens of interest are stored. Thus, our goal is to provide an ontology that represents the administrative entities in biobanking and their relations. We base our ontology development on a set of 53 data attributes called MIABIS, which were in part the result of semantic integration efforts of the European Biobanking and Biomolecular Resources Research Infrastructure (BBMRI). The previous work on MIABIS provided the domain analysis for our ontology. We report on a test of our ontology against competency questions that we derived from the initial BBMRI use cases. Future work includes additional ontology development to answer additional competency questions from these use cases.ResultsWe created an open-source ontology of biobank administration called Ontologized MIABIS (OMIABIS) coded in OWL 2.0 and developed according to the principles of the OBO Foundry. It re-uses pre-existing ontologies when possible in cooperation with developers of other ontologies in related domains, such as the Ontology of Biomedical Investigation. OMIABIS provides a formalized representation of biobanks and their administration. Using the ontology and a set of Description Logic queries derived from the competency questions that we identified, we were able to retrieve test data with perfect accuracy. In addition, we began development of a mapping from the ontology to pre-existing biobank data structures commonly used in the U.S.ConclusionsIn conclusion, we created OMIABIS, an ontology of biobank administration. We found that basing its development on pre-existing resources to meet the BBMRI use cases resulted in a biobanking ontology that is re-useable in environments other than BBMRI. Our ontology retrieved all true positives and no false positives when queried according to the competency questions we derived from the BBMRI use cases. Mapping OMIABIS to a data structure used for biospecimen collections in a medical center in Little Rock, AR showed adequate coverage of our ontology.

Automatic identification of angiogenesis in double stained images of liver tissue

BackgroundTo grow beyond certain size and reach oxygen and other essential nutrients, solid tumors trigger angiogenesis (neovascularization) by secreting various growth factors. Based on this fact, several researches proposed that density of newly formed vessels correlate with tumor malignancy. Vessel density is known as a true prognostic indicator for several types of cancer. However, automated quantification of angiogenesis is still in its primitive stage, and deserves more intelligent methods by taking advantages accruing from novel computer algorithms.ResultsThe newly introduced characteristics of subimages performed well in identification of region-of-angiogenesis. The proposed technique was tested on 522 samples collected from two high-resolution tissues. Having 0.90 overall f-measure, the results obtained with Support Vector Machines show significant agreement between automated framework and manual assessment of microvessels.ConclusionThis study introduces a new framework to identify angiogenesis to measure microvessel density (MVD) in digitalized images of liver cancer tissues. The objective is to recognize all subimages having new vessel formations. In addition to region based characteristics, a set of morphological features are proposed to differentiate positive and negative incidences.

Statistical comparison of color model-classifier pairs in hematoxylin and eosin stained histological images

Color is the most critical information for assessing histological images. However, in literature, there is no standard color space in which a particular color points are represented for computer vision tasks. In this paper, we evaluated 11 color models with three different learning schemas for their performance in classifying tumor-related colors. The color models we studied are CIELAB, CIELUV, CIEXYZ, CMY, CMYK, HSL, HSV, Hunter-LAB, NRGB, RGB, and SCT. With 11 color models, prediction accuracies of three well-known classifiers, namely SVMs, C4.5, and Naïve Bayes, are statistically compared on a large dataset of 3494 Hematoxylin and Eosin (HE) stained histopathologic images. Surprisingly, experiment results show that in contrast to general assumptions, there is no single model that is better than others in every case. However, C4.5 outperformed other two classifiers by obtaining average F-measure of 0.9989. Of 11 color models, we suggest the pair of C4.5-SCT as the most accurate classification framework for tumor identification in HE stained histological images.

A Secure Distributed File System for Medical Image Archiving

The explosion of medical image usage in clinical and research domains brings us a great challenge of securely handling, storing, retrieving and transmitting biomedical images. Medical images are often large files and they have to be stored for a long time if they are part of a patient’s medical record. As medical images usually contain Protected Health Information (PHI), such data is also subjected to various regulations such as HIPAA. Cost effective measures which provide strong security for such data are essential. Therefore, we present a secure and cost effective distributed file system, JigDFS, for archiving medical images/data.

CLARA: an integrated clinical research administration system

Administration of human subject research is complex, involving not only the institutional review board but also many other regulatory and compliance entities within a research enterprise. Its efficiency has a direct and substantial impact on the conduct and management of clinical research. In this paper, we report on the Clinical Research Administration (CLARA) platform developed at the University of Arkansas for Medical Sciences. CLARA is a comprehensive web-based system that can streamline research administrative tasks such as submissions, reviews, and approval processes for both investigators and different review committees on a single integrated platform. CLARA not only helps investigators to meet regulatory requirements but also provides tools for managing other clinical research activities including budgeting, contracting, and participant schedule planning.

Understanding biomedicai research collaborations through social network analysis: A case study

A recent surge of research on social networks and their characteristics has attracted an increasing amount of interests from the community of biomedicine and biomedical informatics. Social network analysis (SNA) methods have been regarded as an effective tool to assess inter- and intra-institution research collaborations in the Clinical Translational Science Award (CTSA) community. In this paper, we present a case study of SNA on the research collaboration networks (RCNs) at the University of Arkansas for Medical Sciences (UAMS) - a CTSA institution. We have applied graph theoretical analyses to the RCNs prior to and after the CTSA award at UAMS. By virtue of quantitative measures, we have obtained valuable insights into the network dynamics and topological characteristics of the research environment. Moreover, through observing the temporal evolution of the RCNs at UAMS, we are able to demonstrate the effectiveness of the CTSA program and its important role in promoting trans-disciplinary collaborative research within an institution.

SIM: A smartphone-based identity management framework and its application to Arkansas trauma image repository

Secure and convenient user identity management is particularly important to the success of EMR, EHR, and PHR systems. Unfortunately, widely-used identity management mechanisms that solely rely on username/password are inadequate to meet the strong security and privacy requirements for protecting sensitive user information and medical data. Two-factor authentication approaches that are more convenient and user friendly than existing solutions have been given top priority in the healthcare sector where the majority of healthcare practitioners and patients are not tech-savvy. In this paper, we present a smartphone-based identity management framework-SIM-to enhance the security and usability of user identity management in healthcare information systems. SIM leverages the popularity and computational power of smartphone. Within the SIM framework, a person employs a smartphone to centrally store and manage her identity credentials and authenticates herself to healthcare applications using two-factor authentication without typing any identity credentials. Moreover, SIM provides patients with a patient-controlled authorization mechanism to help patients manage the accesses to their PHRs in a secure and convenient manner. Using an existing EMR system-Arkansas Trauma Image Repository-as an example, we demonstrate that SIM can be applied to a real-world healthcare information system to enhance its protection of user credentials and sensitive information.

Concept integration from the caTIES to i2b2 using the UMLS semantic network

A tremendous wealth of valuable information is available in the plain text clinical reports and there are variety types of Natural Language Processing (NLP) platforms in place to generate concept codes and mine the reports. The information obtained from the reports has more value if it can be integrated with other clinical and genomics data. The Integrating Biology and the Bedside (i2b2) is being adopted by many institutions. Its open source based scalable framework allows research on genomics and clinical data. In this study, we have shown that any existing information extraction systems can be integrated to i2b2. In order to address this issue, the UMLS semantic network is adopted to map the concept codes generated by the Cancer Text Information Extraction System (caTIES) to i2b2. With the proposed approach, more than 200,000 sample records and 18,000 unique concept codes are made accessible and searchable instantly throughout the i2b2 infrastructure.

Design of a lattice-based access control scheme

We survey the literature for access control schemes in a user hierarchy. Some schemes have already been shown to be insecure or incorrect. Many schemes assume very restrictive subordinating relationships existing in a hierarchy where users are grouped into partially ordered relationships without taking resources into consideration. We believe that a practical access control scheme should support access control in a lattice where users and resources are both together grouped into partially ordered relationships. In this paper, we present a scheme to achieve this goal. We also study existing schemes for their efficiency and performance. Based on the results of the study, we design an efficient scheme to support dynamic key management.