gnTEAM » Search Results » “Biology” http://gnteam.cs.manchester.ac.uk Text extraction, analytics, mining Fri, 05 Mar 2021 17:44:55 +0000 en-US hourly 1 https://wordpress.org/?v=4.2.37 Constructing a molecular interaction network for thyroid cancer via large-scale text mining of gene and pathway events. http://gnteam.cs.manchester.ac.uk/publication/298004-constructing/ http://gnteam.cs.manchester.ac.uk/publication/298004-constructing/#comments Mon, 07 Mar 2016 11:11:33 +0000 http://gnteam.cs.manchester.ac.uk/?post_type=publication&p=1644 The post Constructing a molecular interaction network for thyroid cancer via large-scale text mining of gene and pathway events. appeared first on gnTEAM.

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]]> http://gnteam.cs.manchester.ac.uk/publication/298004-constructing/feed/ 0 Disentangling the multigenic and pleiotropic nature of molecular function. http://gnteam.cs.manchester.ac.uk/publication/298002-disentangling/ http://gnteam.cs.manchester.ac.uk/publication/298002-disentangling/#comments Mon, 07 Mar 2016 11:11:15 +0000 http://gnteam.cs.manchester.ac.uk/?post_type=publication&p=1643 The post Disentangling the multigenic and pleiotropic nature of molecular function. appeared first on gnTEAM.

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]]> http://gnteam.cs.manchester.ac.uk/publication/298002-disentangling/feed/ 0 Protein Function Assignment using Term-based Support Vector Machines. BioCreative Task Two 2004 http://gnteam.cs.manchester.ac.uk/publication/rice2004protein/ http://gnteam.cs.manchester.ac.uk/publication/rice2004protein/#comments Thu, 02 Jul 2015 12:13:47 +0000 http://gnode.dev/?post_type=publication&p=1549 The post Protein Function Assignment using Term-based Support Vector Machines. BioCreative Task Two 2004 appeared first on gnTEAM.

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]]> http://gnteam.cs.manchester.ac.uk/publication/rice2004protein/feed/ 0 Mining term associations and events from bio-literature http://gnteam.cs.manchester.ac.uk/project/mining-term-associations-and-events-from-bio-literature/ http://gnteam.cs.manchester.ac.uk/project/mining-term-associations-and-events-from-bio-literature/#comments Fri, 26 Jun 2015 14:02:49 +0000 http://gnode.dev/?post_type=project&p=273 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…

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]]> 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 and links among various biological entities such as proteins, genes, species, cells, and experiments. The work was partially funded by BBSRC (“Mining term associations to support knowledge discovery in biology”) to explore suitable technologies for modelling user-elicited biological text mining scenarios to support hypothesis generation, and builds on a previous BBSRC project (“Protein Functional Classification using Text Data-mining”) that has developed automatic text-based classification of proteins to functional categories (based on the Gene ontology) using machine learning techniques and various textual features.
We are specifically interested in extraction of various molecular events, including gene expressions (see GETM), positive and negative regulations, bindings, etc. (see BioNLP as well as “Mining molecular events and their context” below).

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]]> http://gnteam.cs.manchester.ac.uk/project/mining-term-associations-and-events-from-bio-literature/feed/ 0 Dr Jean-Marc Schwartz http://gnteam.cs.manchester.ac.uk/staff/jmschwartz/ http://gnteam.cs.manchester.ac.uk/staff/jmschwartz/#comments Fri, 26 Jun 2015 12:18:58 +0000 http://gnode.dev/?post_type=staff&p=247 The post Dr Jean-Marc Schwartz appeared first on gnTEAM.

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]]> http://gnteam.cs.manchester.ac.uk/staff/jmschwartz/feed/ 0 Prof David Robertson http://gnteam.cs.manchester.ac.uk/staff/prof-david-robertson/ http://gnteam.cs.manchester.ac.uk/staff/prof-david-robertson/#comments Fri, 26 Jun 2015 12:16:53 +0000 http://gnode.dev/?post_type=staff&p=246 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…

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]]> 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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]]> http://gnteam.cs.manchester.ac.uk/staff/prof-david-robertson/feed/ 0 Dr Chengkun Wu http://gnteam.cs.manchester.ac.uk/staff/chengkunwu/ http://gnteam.cs.manchester.ac.uk/staff/chengkunwu/#comments Fri, 26 Jun 2015 10:13:22 +0000 http://gnode.dev/?post_type=staff&p=244 I am currently a PhD student in Doctoral Training Centre in Integrative Systems Biology at the University of Manchester. I’m now working on the text mining and logical modelling for systems biology.

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]]> I am currently a PhD student in Doctoral Training Centre in Integrative Systems Biology at the University of Manchester.
I’m now working on the text mining and logical modelling for systems biology.

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]]> http://gnteam.cs.manchester.ac.uk/staff/chengkunwu/feed/ 0 Prof Robert Stevens http://gnteam.cs.manchester.ac.uk/staff/rstevens/ http://gnteam.cs.manchester.ac.uk/staff/rstevens/#comments Thu, 25 Jun 2015 16:15:44 +0000 http://gnode.dev/?post_type=staff&p=238 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…

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]]> 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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]]> http://gnteam.cs.manchester.ac.uk/staff/rstevens/feed/ 0 Natural Language Processing for Clinical Data: Continuous Success at i2b2 Challenges http://gnteam.cs.manchester.ac.uk/challenges/i2b2/ http://gnteam.cs.manchester.ac.uk/challenges/i2b2/#comments Tue, 23 Jun 2015 11:57:59 +0000 http://gnode.dev/?page_id=78 2011 – This year we took again part in the annual i2b2 shared task, an international text mining challenge in the clinical/health-care domain. The team composed of members from University of Novi Sad (Kovacevic, A.) and University of Manchester (Dehghan, A., Nenadic G. and Keane, J.). The aim of the challenge…

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]]> 2011 – This year we took again part in the annual i2b2 shared task, an international text mining challenge in the clinical/health-care domain. The team composed of members from University of Novi Sad (Kovacevic, A.) and University of Manchester (Dehghan, A., Nenadic G. and Keane, J.). The aim of the challenge (Fifth/i2b2, Track II: Sentiment analysis) was to classify at line-level, statements in suicide notes into 15 categories (i.e., emotions and expressions).

The challenge was most interesting this year to say the least. Despite some surprises, we managed to rank eight out of 26 participating teams. We were also one of only 5 teams invited to give a talk at the workshop and a full text publication.

2010 – A team of staff from Manchester’s School of Computer Science (Irena Spasic, Farzaneh Sarafraz, John A. Keane and Goran Nenadic) took again part in the Third i2b2 shared task. The challenge was organised by Informatics for Integrating Biology and the Bedside, i2b2.

This year, the aim was the extraction of medication-related information from narrative patient records. For each medication mention, details (such as medication name, dosage, reason for taking, frequency, duration etc.) were provided by the participants and have been evaluated against a manually extracted godl standard, which was generated by collaborative annotation by all participating teams.

We are pleased to announce that our team repeated the last year’s success and was among the top ranked teams for the second year running. Overall, the team was ranked third out of 19 teams taking part, with the same significance level as the second ranked team.

2009 – More information on the 2009 challenge can be found at: i2b2 Web site: the Third Shared Task in Natural Language Processing for Clinical Data: Medication ExtractionChallenge.

2008 – Our team was announced the winner in one of the two tasks in the Second shared challenge in Natural Language Processing for Clinical Data: Obesity Challenge: Who’s obese and what co-morbidities do they (definitely/likely) have?

The goal of the 2008 challenge was to evaluate NLP systems on their ability to recognise whether a patient is obese and what co-morbidities they exhibit. The data consisted of hospital discharge summaries, and obesity information and co-morbidities were marked at a document level as present, absent, questionable or unmentioned. For each patient, both textual judgments (what the text explicitly states about obesity and co-morbidities) and intuitive judgments (what the text implies about obesity and co-morbidities) were provided by the participants.

There were 28 teams taking part in the 2008 challenge. Our TEAM was announced as the winner for the textual task (97.2% accuracy) and we were ranked 7th in the intuitive judgement task (95.7% accuracy).

Publications

  • Kovacevic, A., Dehghan, A., Keane, J., Nenadic, G.: Topic Categorisation of Statements in Suicide Notes with Integrated Rules and Machine Learning, J Biomed Informatics Insight, In press 2012 (link)
  • Spasic, I., Sarafraz, F., Keane, J., Nenadic, G.: Medication Information Extraction with Linguistic Pattern Matching and Semantic Rules, Proceedings of the i2b2 2009 Workshop.
  • Yang, H., Spasic, I., Keane, J., Nenadic, G.: A Text Mining Approach to the Prediction of a Disease Status from Clinical Discharge Summaries, J. of American Medical Informatics Association, 16(4):596-600; (link)

Links:

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]]> http://gnteam.cs.manchester.ac.uk/challenges/i2b2/feed/ 0 Information for prospective postgraduate students http://gnteam.cs.manchester.ac.uk/contact/prospective-postgraduates/ http://gnteam.cs.manchester.ac.uk/contact/prospective-postgraduates/#comments Mon, 22 Jun 2015 12:21:04 +0000 http://gnode.dev/?page_id=62 The post Information for prospective postgraduate students appeared first on gnTEAM.

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  • 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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