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…

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…

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).…

Detecting and analysing contrasts and contradictions in scientific texts is essential for suggesting further research potentials and discoveries. Finding contrasts and contradictions in text by means of automatic methods is a relatively new area in text mining. Specifically, most biological text mining research has so far focused on mining affirmative…

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…

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…

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…

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…

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…