Prof Goran Nenadic

Prof Goran Nenadic

Professor of Computer Science, research leader

Biography

Prof Goran Nenadic is Professor in the School of Computer Science, University of Manchester, a group leader in the Manchester Institute of Biotechnology (MIB) and Health eResearch Centre (HeRC).
Keywords
Website

http://personalpages.manchester.ac.uk/staff/GNenadic/

Publications

Projects

  • Veterinary text mining

    The Small Animal Veterinary Surveillance Network (SAVSNET) is an initiative from the British Small Animal Veterinary Association and the University of Liverpool to collect and analyse companion animal data. SAVSNET collects pets health data in real time from diagnostic laboratories and veterinary consultations across the UK (from ~500 veterinary premises). Given that consultation…

  • Healthcare text mining projects: mining clinical narratives and patient-generated data

    We currently run a number of projects to extract various structured data from unstructured clinical narratives and electronic healthcare records (EHRs). In previous projects we have developed a combination of rule-based and machine-learning methods to identify diseses that a patient has or does not have (“disease status”), including identification of…

  • Integration of Dynamic Documentation Knowledge Services into Siemens Framework

    This project is carried out in collaboration with the Clinical Knowledge Management Research Team at Manchester. The team, originated by Professor Alan Rector, has a thirty-year history of leading innovations in clinical user interfaces, development of health terminology/ontologies, and medial records. The Clinical Knowledge Management Research Team and Siemens Medical…

  • Health eResearch Centre (HeRC) – harnessing electronic health data to improve care for patients and communities

    Our research on clinical text mining, processing patient generated data and building interoperable clinical data processing infrastructures is part of a new multimillion-pound centre of excellence in that has been awarded to a consortium led by The University of Manchester. The consortium brings together partners from academia, the NHS, local…

  • Mining molecular interaction data and its context

    The project involves extraction of the context of molecular interaction data from the scientific literature. So far, little attempt has been made to capture the context of molecular interaction, how reliable it is, what is the nature of interaction etc. The project aims to study the way findings, experiments and…

  • Logical modelling of molecular interactions in the development of thyroid cancer

    The main goal of the project is to generate new hypotheses for the understanding and treatment of thyroid cancer with the help of Text Mining and Logical Modelling. Text Mining is used to extract information related to the molecular interactions for thyroid cancer from the biomedical literature (based on BioContext).…

  • Investigating Data Quality Aspects of Question and Answer Reports

    As the quantity of available data increases, the level of “quality” varies significantly, and this becomes a critically important factor for the effectiveness of organisations and individuals. Most of the business and scientific data is represented in unstructured and semi-structured formats. However, most current data quality methodologies work solely on…

  • Named-entity recognition and term mining

    Recognizing terms and named entities in research articles and mapping them to unique identifiers is an important first step in most text mining software. This is a challenging task because of ambiguity and variation in how entities and concepts are named and used in particular in the biological literature. Our…

  • Mining term associations and events from bio-literature

    This is a long-term project that aims at developing text mining methods that can provide efficient and sophisticated knowledge acquisition, offer plausible hypotheses for testing, prevent unnecessary repetition of previous work, and help in experimental design for specific research scenarios. We investigate various text mining approaches to establishing literature-based associations…

  • Mining bioinformatics service descriptions

    There are a number of services and resources available to the bioinformatics community, but meta-data that describe them is typically scarce. This project aims to develop text mining techniques to automatically describe, locate, retrieve and reason about bioinformatics services and resources. We investigate methods that extract descriptions from various document…

  • Integration of text and data mining in life sciences

    There have been numerous efforts to provide tools for storing, extracting and analysing data in life sciences. Interoperability and integration of such efforts is a challenging issue, not only technically (e.g. different formats, protocols, encodings) but also more importantly semantically. We are involved in a number of community-driven initiatives to…

  • Blog sentiment analysis

    Sentiment analysis is the extraction of attitudes and opinions from human-authored documents. The capture and analysis of such attitudes and opinions in an automated and structured fashion might offer a powerful technology to a number of problem domains, including business intelligence, marketing, national security, and crime prevention. This project aims…