Browsing by Author "Radovanovic, Aleksander"

Now showing 1 - 5 of 5
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    Database for exploration of functional context of genes implicated in ovarian cancer
    (Oxford Journals, 2009) Kaur, Mandeer; Radovanovic, Aleksander; Essack, Magbubah; Schaefer, Ulf; Maqungo, Monique; Kibler, Tracey; Schmeier, Sebastian; Christoffels, Alan; Narasimhan, Kothandaraman; Choolani, Mahesh; Bajic, Vladimir B.
    Ovarian cancer (OC) is becoming the most common gynecological cancer in developed countries and the most lethal gynecological malignancy. It is also the fifth leading cause of all cancer-related deaths in women. The identification of diagnostic biomarkers and development of early detection techniques for OC largely depends on the understanding of the complex functionality and regulation of genes involved in this disease. Unfortunately, information about these OC genes is scattered throughout the literature and various databases making extraction of relevant functional information a complex task. To reduce this problem, we have developed a database dedicated to OC genes to support exploration of functional characterization and analysis of biological processes related to OC. The database contains general information about OC genes, enriched with the results of transcription regulation sequence analysis and with relevant text mining to provide insights into associations of the OC genes with other genes, metabolites, pathways and nuclear proteins. Overall, it enables exploration of relevant information for OC genes from multiple angles, making it a unique resource for OC and will serve as a useful complement to the existing public resources for those interested in OC genetics.
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    DDEC: Dragon databaseof genes implicated in esophageal cancer
    (BioMed Central, 2009) Essack, Magbubah; Radovanovic, Aleksander; Schaefer, Ulf; Schmeier, Sebastian; Seshadri, Sundararajan V.; Christoffels, Alan; Kaur, Mandeep; Bajic, Vladimir B.
    Esophageal cancer ranks eighth in order of cancer occurrence. Its lethality primarily stems from inability to detect the disease during the early organ-confined stage and the lack of effective therapies for advanced-stage disease. Moreover, the understanding of molecular processes involved in esophageal cancer is not complete, hampering the development of efficient diagnostics and therapy. Efforts made by the scientific community to improve the survival rate of esophageal cancer have resulted in a wealth of scattered information that is difficult to find and not easily amendable to data-mining. To reduce this gap and to complement available cancer related bioinformatic resources, we have developed a comprehensive database (Dragon Database of Genes Implicated in Esophageal Cancer) with esophageal cancer related information, as an integrated knowledge database aimed at representing a gateway to esophageal cancer related data. Manually curated 529 genes differentially expressed in EC are contained in the database. We extracted and analyzed the promoter regions of these genes and complemented gene-related information with transcription factors that potentially control them. We further, precompiled text-mined and data-mined reports about each of these genes to allow for easy exploration of information about associations of EC-implicated genes with other human genes and proteins, metabolites and enzymes, toxins, chemicals with pharmacological effects, disease concepts and human anatomy. The resulting database, DDEC, has a useful feature to display potential associations that are rarely reported and thus difficult to identify. Moreover, DDEC enables inspection of potentially new 'association hypotheses' generated based on the precompiled reports. We hope that this resource will serve as a useful complement to the existing public resources and as a good starting point for researchers and physicians interested in EC genetics.
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    DDESC: Dragon database for exploration of sodium channels in human
    (BMC Cancer, 2008) Sagar, Sunil; Kaur, Mandeep; Dawe, Adam; Seshadri, Sundararajan V.; Christoffels, Alan; Schaefer, Ulf; Radovanovic, Aleksander; Bajic, Vladimir B.
    Sodium channels are heteromultimeric, integral membrane proteins that belong to a superfamily of ion channels. The mutations in genes encoding for sodium channel proteins have been linked with several inherited genetic disorders such as febrile epilepsy, Brugada syndrome, ventricular fibrillation, long QT syndrome, or channelopathy associated insensitivity to pain. In spite of these significant effects that sodium channel proteins/genes could have on human health, there is no publicly available resource focused on sodium channels that would support exploration of the sodium channel related information. We report here Dragon Database for Exploration of Sodium Channels in Human (DDESC), which provides comprehensive information related to sodium channels regarding different entities, such as "genes and proteins", "metabolites and enzymes", "toxins", "chemicals with pharmacological effects", "disease concepts", "human anatomy", "pathways and pathway reactions" and their potential links. DDESC is compiled based on text- and data-mining. It allows users to explore potential associations between different entities related to sodium channels in human, as well as to automatically generate novel hypotheses. DDESC is first publicly available resource where the information related to sodium channels in human can be explored at different levels.
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    DDPC: Dragon database of genes associated with prostate cancer
    (Oxford Journals, 2011) Maqungo, Monique; Kaur, Mandeep; Kwofie, Samuel K.; Radovanovic, Aleksander; Schaefer, Ulf; Schmeier, Sebastian; Oppon, Ekow; Christoffels, Alan; Bajic, Vladimir B.
    Prostate cancer (PC) is one of the most commonly diagnosed cancers in men. PC is relatively difficult to diagnose due to a lack of clear early symptoms. Extensive research of PC has led to the availability of a large amount of data on PC. Several hundred genes are implicated in different stages of PC, which may help in developing diagnostic methods or even cures. In spite of this accumulated information, effective diagnostics and treatments remain evasive. We have developed Dragon Database of Genes associated with Prostate Cancer (DDPC) as an integrated knowledgebase of genes experimentally verified as implicated in PC. DDPC is distinctive from other databases in that (i) it provides pre-compiled biomedical text-mining information on PC, which otherwise require tedious computational analyses, (ii) it integrates data on molecular interactions, pathways, gene ontologies, gene regulation at molecular level, predicted transcription factor binding sites on promoters of PC implicated genes and transcription factors that correspond to these binding sites and (iii) it contains DrugBank data on drugs associated with PC. We believe this resource will serve as a source of useful information for research on PC.
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    SIP presence location service
    (Telkom, 2005) Wu, Wilson; Radovanovic, Aleksander; Tucker, William David
    This paper presents an innovative use of the Session Initiation Protocol (SIP) for the subscription and notification of geographic information in order to provide a privacy concerned location-based service. SIP is a signaling protocol used for establishing sessions in an IP network. It has been widely used for Internet conferencing and telephony. This research project aims to enhance the SIP presence model in order to protect sensitive geographic information. To achieve this goal, we thoroughly analyzed existing Location-Based Services (LBS), reviewed LBS designs’ pitfalls and identified several key privacy requirements. Based on this research, we presented a SIP flow that meets the privacy requirements. This SIP message flow includes SUBSCRIBE, NOTIFY and PUBLISH messages. A data format to carry geographic location information has also been introduced. The data format is based on Presence Information Data Format (PIDF). We define it as Location-enhanced PIDF, or LPIDF. LPIDF contains geographical information objects. We hope that the outcome of this research project will provide rich, convenient, privacy concerned architecture for LBS. Because LPIDF is based on SIP, this approach can be easily integrated into IP telephony services. LPIDF enables personalization of the Location-Based services address user privacy concerns and hereby increase their satisfaction.