gnTEAM » Search Results » “Bioinformatics”

Dr Idoia Gomez-Paramio

gnenadic — Thu, 19 Jul 2018 15:25:17 +0000

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Mining molecular interaction data and its context

admin — Thu, 02 Jul 2015 10:12:44 +0000

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 knowledge about molecular interactions is presented in the literature, and in particular how contextual information that details molecular interactions are encoded and presented. The project implements a text mining framework to extract (from full-text articles) contextual information and link it with data in other resources to support informed decisions for understanding the complexity of interactions. The project is collaboration with Pfizer and thus the focus is placed on pharmaceutically relevant data sets including various pathogens such as HIV, hepatitis viruses, malaria etc.
In previous work (with M. Gerner, S. Farzaneh, C. Bergman) we have developed BioContext, a system for extracting and integrating information about molecular processes in biomedical articles. Using the data extracted by BioContext, it is possible to get an overview of a range of biomolecular processes relating to a particular gene or anatomical location. The current project is part of Dan’s PhD, with Prof D. Robertson (Bioinformatics) and Drs B. Sidders (Pfizer) and G. Nenadic as supervisors.

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Named-entity recognition and term mining

admin — Fri, 26 Jun 2015 14:09:30 +0000

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 main focus in this theme is the identification of gene and protein names. This project aims to conduct a critical evaluation of open source gene name identification systems in full-text articles and associated supplementary data files, to integrate gene name identification systems into general bioinformatics workflows, and to develop real-time methods for gene name identification that will assist authors in the publication process. This project will also establish best practices, provide middleware for gene name identification, and integrate gene name identification into the Semantic Web infrastructure for the life sciences. We have been collaborating with J. Hakenberg to improve GNAT.
We are also interested in identification of other semantic classes, such as species names (see LINNAEUS and its demo page), anatomical locations (see GETM), clinical concepts (see i2b2), etc.

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Mining bioinformatics service descriptions

admin — Fri, 26 Jun 2015 13:59:27 +0000

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 types (articles, reviews, application notes, email archives, discussion forums, etc), and map them to service descriptions using both general service ontologies and domain-specific ontologies. As a working and target environment, the project uses the myGRID/Taverna infrastructure.

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Integration of text and data mining in life sciences

admin — Fri, 26 Jun 2015 13:57:52 +0000

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 provide better integration for life science research.
One initiative is to provide harmonised ways for representing and tagging named entities in the life science literature. We are proposing to establish common document formats that facilitate the exchange of annotation results contained in the literature as a complementary approach to the development of interoperable tools. We work towards (a) recommendations for a common syntax to embody entity mentions in publishers’ document formats (e.g., into PMC), and (b) provision of a common way to reference semantic types. The initial results have been implemented in the IeXML proposal, which has already been used in some community-wide projects (e.g. CALBC). The original IeXML paper is available here.
Involved: D. Rebholz (EBI), G. Nenadic
Another initiative is to use ontologies and text mining to integrate and mark up data (both structured and unstructured) and provide semantics-based faceted browsing to help users navigate, query and retrieve data. The Ontogrator platform has been developed by the NERC Environmental Bioinformatics Centre and the University of Manchester, with a pilot implementation developed in collaboration with the Genomic Standards Consortium (GSC) that includes integrated content from the StrainInfo, GOLD, CAMERA, Silva and Pubmed databases.
Involved: D. Field (NEBC), N. Morrison (Manchester), D. Hancock, L. Hirschman, G. Nenadic, et al.
As part of the BBSRC-funded pubmed2ensembl project, we have developed a customised and extended version of the Ensembl BioMart by adding gene-related publication information, i.e. PubMed-IDs and PubMed Central-IDs including URL link-outs and other information. The pubmed2ensembl BioMart has an enhanced interface that permits to carry out interactive full-text search queries via NCBI’s Entrez Utilities (eUtils), whose search results are applied as an additional filter on the mart datasets. The system also provides DAS link-outs into the Ensembl Genome Browser, where a custom DAS track summarises the publication data that have been accumulated on a per gene basis.
Involved: J. Baran, C. Bergman, G. Nenadic, M. Gerner

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Prof David Robertson

admin — Fri, 26 Jun 2015 12:16:53 +0000

My research is primarily focussed on computational biology and molecular evolution. Evolution is what explains life and its many forms, and so its study is of central importance in biological research. Molecular evolution is the study of evolution at the molecular level from genes to genomes to systems biology, while computational biology is the data-focussed use of computers in biological research to address analytical and theoretical questions/hypotheses.

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Dr Casey Bergman

admin — Fri, 26 Jun 2015 09:10:28 +0000

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Prof Robert Stevens

admin — Thu, 25 Jun 2015 16:15:44 +0000

I am a professor of computer science in the Bio-Health Informatics Group in the School of Computer Science at the University of Manchester. I have a B.Sc. in biochemistry; an M.Sc. in biological Computation; and a D.Phil. in Computer Science: A blend of Biology and Computer Science, that I use within my main research area of Bioinformatics. I am also a member of the Information Management Group where I work with colleagues on both e-Science and description logics. I also have close affiliation with the Bioinformatics Groups from the Faculty of Life Sciences.

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Information for prospective postgraduate students

admin — Mon, 22 Jun 2015 12:21:04 +0000

General information

We are always keen to have postgraduate research students in various areas of text mining and natural language processing. As a rule of thumb, you will need to have an xmaplesxcellent first degree in computer science or related area (e.g. computational lingustics, mathematics, physics, bioinformatics), with very good programming experience and some experience in natural language processing (e.g. final year project, summer internship, an ad-hoc project). An MSc or publications in a related area will be also a distinctive advanatage try terrorism essay for free.

The main theme of our research is feature engineering from unstructured documents written in natural languages. We investigate methodologies for the extraction of both explicit and implicit features from large collections of textual documents. Features can be terms, names, relations, co-occurances, events, etc. Once engineered from text, the features can be used to provide understanding and reasoning over knowledge (e.g. by applying machine learning or data mining) – this discipline is referred to as text analytics, text mining or more generally natural language processing (NLP).
Themes

Here are some core text mining themes (please see below for details) that are currently the focus in our TEAM:
- Text analytics and sentiment analysis: identification of subjective opinion and sentiment features from user-generated content (e.g. blog mining, tweets, etc.);
- Extracting negations, contrasts and contradictions: identification of utterances that are negated, or contrast or contradict some other expressions (both explicit and implicit);
- Concept mining and structuring: learning and identification of concepts and terminology from text, including their structuring (internal and external);
- Temporal text analytics: identification of temporal expressions and their scope in text;
- Integrated text and data mining: combining the results from different perspectives using various methods from machine learning;
- Text processing midleware for the Semantic Web: building an infrastructure to support building text mining solutions for the Semantic Web (identification of concepts, links, etc);
and these are preferred application areas:
- Biology and biomedicine (molecular interactions, cancer studies, characterisation of molecular events, etc.)
- Bioinformatics and computational biology (tools, services, resources, methods)
- Clinical medicine and health-care (clinical decision support, quality of life monitoring)
- E-science, e-commerce and e-government (e.g. monitoring, tracking, dissemination of information)
- Engineering (knowledge management)
You would typically ‘select’ a topic that consist of a particular theme in a specific application area. I’d be also happy to consider proposals in the areas of multi-lingual text mining and NLP for Serbian.
Application steps

You will be expected to have passion for text processing, in addition to an excellent first degree in computer science or related area. Some experience in natural language processing is very useful, whereas very good programming experience (in a combination of programming languages) is a must. If you belive you’ve got all these, send an email to Goran Nenadic (see below) with a full CV and a brief note as why you would like to do PhD in our TEAM. Please allow some time for us to reply. Contact email: G.Nenadic@manchester.ac.uk.
Funding

PhD studies are between 3 and 4 years, typically closer to 4 than to 3 years. There is only one route for securing funding: the candidate needs to be outstanding. There are 3 possible sources of funding:
- specific, pre-defined projects (NONE CURRENTLY),
- funding from the School of Computer Science (see here for details) and
- external funding (private, external bodies – e.g. foreign governments, etc).
Environment

The School of Computer Science is one of the leading Schools in the UK reknown for the excellence of its research. The world’s first computer with internal memory was build in the School and Alan Turing has laid the foundations of Computer Science and Artificial intelligence while in Manchester. The international reputation of our research reflects on its high ranking in the last national Research Assessment Exercise (RAE), which places the School among the best five Computer Science departments in the UK and top in England for research power. The School has a vibrant research environment with more than 150 PhD students, 90 research staff and 70 academic staff.

Our research TEAM is part of the Text Mining/NLP research group, which hosts the UK National Centre for Text Mining. We are also affiliated to the Manchester Interdisciplinary BioCentre. The team is vibrant, diverse and very much international.

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Projects

admin — Mon, 22 Jun 2015 12:04:33 +0000

Our research projects are focused on developing techniques for large-scale extraction and management of un- and semi-structured textual resources.

We focus on:

Health-related information synthesis
- Synthesis of information from unstructured electronic health-care records, patient narratives and literature to support clinical decision support please doing paper for me
- Clinical documentation, text mining and terminology management
- Integration and analytics of health-care linked-data
- Sentiment mining of health-related social media
- Temporal text mining
Large-scale extraction and contextualization of biomolecular events
- Identification of conflicting statements in scientific texts
- Extraction of host-pathogen interactions
Mining of scientific methodologies from literature
- Capturing and analysing the use of in-silico experimental methods in computational biology and bioinformatics

Our projects

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