Structure Characterisation and Automation for Mass Spectrometry
This project is to investigate the techniques that use textures to enhance the
exhibition of surface details in 3D computer generated images. It is generally
recognised that one of the major differences between real world images and
computer synthesized images lies in the exhibition of high-frequency visible
surface details. This project will be an effort to bridge this major gap. The
principal objective of this project is to develop a key technique called
Amplifiable Bi-directional Texture Function (ABTF). ABTF is designed to
generate textures on 3D surface models. Compared with the conventional texture
mapping/synthesis techniques, ABTF will have the following strengths: 1) It
will not compromise on texture quality in close-up views; 2) It will accurately
display significant visual effects of fine surface details, including
self-shadowing and occlusion, inter-reflection and silhouettes, under different
viewing/lighting settings. 3) It will take into consideration the influence
from the variations of target surface curvatures for the correct synthesis and
display of textures. The ABTF synthesis will be based on a hybrid image
modelling and rendering approach. Given multiple views of a texture sample, it
will recover the underlying geometries of the texture and use them as surface
details for the target surface. For high visual fidelity purpose, the colours
of the target surface will be obtained from the images of the multiple views
through a quick search scheme for achieving high quality and fast performance.
The project has a wide range of potential applications. In fact, the range of
use of 3D high fidelity images in different businesses and fields is
surprisingly broad, which suggests a wide range of possible commercial
application. Many 3D-related businesses have significant presence in the UK,
and are playing an active role in the global market, e.g. computer games,
computer animation, broadcast television, mobile communication, web design,
etc., all of which are facing demands for improved image quality to take full
advantage of continual advances in display technology.
Researchers:
F Dong and D Chen (Brunel).
Support:
EPSRC
Duration: 2005-2008
DNA microarray technology has enabled biologists to study all
the genes within an
entire organism to obtain a global view of gene
interaction and regulation.
However, the technology is still early in its development, and errors may be
introduced at each of the main stages of the microarray process: spotting,
hybridisation, and scanning. Consequently the microarray image data collected
often contain errors and noise, which will then be propagated down through all
later stages of processing and analysis.
Although there is recently much
research on how to detect and eliminate these variations and errors, the
progress has been slow.
The proposed project explores novel methods for processing
microarray image data by
reconstructing background noise of the microarray chip.
It brings
together expertise from the disparate fields of image processing, data mining
and molecular biology to make an interdisciplinary attempt in advancing the
state of art in this important area. It is particularly timely since there is
an urgent need to have image analysis software that can save both time and
labour as well as provide high-quality image data.
Researchers:
X Liu, Y Li, Z Wang and K Fraser (Brunel), and
P Kellam (UCL).
Support:
EPSRC
Duration: 2006-2008
This proposal aims to develop a system for video annotation, i.e. assigning
meaningful semantic labels to video units. As opposed to many previous
studies where ad-hoc concepts such as 'grasses', 'sky' and 'explosion' are
adopted, we will address the problem from a different perspective by combining
human activity and visual context. On the one hand, humans are usually the
subjects of video semantics, e.g. the doer of an action. Their presence,
activities and interactions are often the key factors to video contents. On the
other hand, context, the physical or informative environment or situation where
human activities are undertaken, can greatly clarify ambiguity and reduce
complexity in video content understanding. Based on our previous work on human
face processing and semantic video analysis, we will develop new algorithms and
methods for appearance based non-rigid object (e.g. face) tracking, incremental
and robust person-specific facial model updating, and unsupervised automatic
contextual analysis. The system will detect and track human faces and provide
probabilistic descriptions of each individual human face as well as their
trajectories in a video. It will also formulate person-specific facial
appearance models online by incrementally and robustly updating a generic
facial model. Meanwhile, the system will perform unsupervised visual context
analysis from low-level features on each video segments.
Researchers:
Y Li (Brunel).
Support:
EPSRC
Duration: 2005-2008
The proposed research will examine an alternative computational
framework called Simultaneous Modelling and Clustering (SMC)
that will support the automatation
of the gene expression time series analysis process. The SMC would cluster gene
expression variables by scoring a candidate cluster on the predictive ability
of a model that is built from variables within that cluster. A balanced
optimisation strategy will be developed to allow quality models to be generated
while managing to converge quickly. Novel scalable algorithms will be proposed
to analyse gene expression time series involving thousands of variables.
A systematic evaluation of the SMC methods will be
performed on a variety of virus and host interaction gene expression time
series using
bioinformatics resources such as VIDA and BIOMAP.
Parallel algorithms will be designed for
running on a computer farm to speed up the process.
A collection of software tools for modelling
gene expression time series will be made available to
life scientists.
Researchers:
X Liu, M Hirsch, S Swift and A Tucker (Brunel),
P Kellam (UCL), N Martin (Birkbeck),
C Orengo (UCL).
Support:
BBSRC
Duration: 2005-2008
This research programme will combine the expertise of a consortium of
biologists, control engineers, computer scientists, mathematicians
and statisticians to develop and validate a broad range of
mathematical modelling tools for DNA microarray analysis, a key area
of post-genomic research. The analysis will focus mainly on large
robust microarray datasets obtained from a model bacterium,
Streptomyces coelicolor, and a simple model eukaryotic system,
herpes virus lytic replication. Our goal is to produce generic
tools for modelling changes in cellular transcriptomes following
induction of defined cellular processes and gain new biological
insight into the gene regulation networks of the two representative
model systems. These technologies and appropriate training will
be made available to the wider UK functional genomics community.
Researchers:
X Liu and V Vinciotti (Brunel),
P Kellam (University College London),
D Broomhead, M Muldoon (Manchester University),
D Lowe and I Nabney
(Aston University),
E Wit, M Titterington, I Molchanov,
A Nobile and K Vass (Glasgow University),
C Smith (Surrey University), and
O Wolkenhauer (Rostock University).
Support: The
BBSRC/EPSRC
Exploiting Genomics Initiative.
Duration: 2003-2008
Web:
The MARIE Project
The main aim of the project is to develop original methodologies for
predicting the functions of uncharacterised genes. Our approach will be
to integrate gene expression data with protein family, function and
pathway or process data. Connection of these two important fields will
significantly enhance the value of both datasets. The project will be
developed using real biological data from existing projects using both
Affymetrix and spotted array platforms.
Two specific biological questions with direct implications for human health
and well-being are addressed: the DNA damage response and
the cellular response to viral infection.
We will integrate the related data
into a data warehouse and develop novel data mining protocols which
use prior knowledge of protein family and functions to facilitate
analysis of co-expressed genes. Finally, to cope with this new level
of complex data integration, we will develop innovative visualisation
tools to represent the pathways, processes and interactions suggested
by data mining.
Researchers:
C Orengo, S Nagl, D Jones, B Buxton and P Kellam
(UCL),
J Thornton and A Brazma (European Bioinformatics
Institute),
X Liu, S Swift and A Tucker (Brunel), M Hubank (Institute of Child
Health),
and N Martin (Birkbeck College).
Support:
The Wellcome Trust
Functional Genomics Development Initiative.
Duration: 2003-2007
Web:
The BIOMAP Project
Digital Libraries make information directly available to users via both
Intranets and the Internet. Recent research has demonstrated the need for
digital libraries to bridge gaps between the producers and the users of the
information by providing personalised services. In particular, Web-based
library catalogues serves as the major medium for such engagement. To this end,
the project aims to develop a personalised Web-based library catalogue to
accommodate users individual differences, especially cognitive styles.
Empirical studies will be conducted to examine the effects of cognitive style
on users information seeking, and generate guidelines to help designers
understand how users with different cognitive styles interact with the
personalised Web-based library catalogues.
Researchers:
S Chen, E Frias-Martinez, X Liu,
R Macredie (Brunel), and G Magoulas (Birkbeck).
Support:
AHRC
Duration: 2003-2007
The principal motivation for this proposed project is threefold. First, many
engineering systems have performance requirements naturally stated in terms
of the upper bounds on the steady-state variance values. Secondly, a
satisfactory engineering system should possess multiple desired performances
such as exponential stability, good steady-state behaviour, robustness to
modelling uncertainties, acceptable disturbance rejection attenuation level,
reliability, etc. Thirdly, owing to the advances in digital computers and
the complexity of modelling, filtering and control for nonlinear stochastic
systems have been developed and used in numerous applications. In this
research project, the variance-constrained multiobjective stochastic control
and filtering theory will be studied extensively for a class of nonlinear
stochastic systems. Appropriate time-domain state-space design approaches
and criteria will be investigated in detail based on stochastic analysis and
nonlinear system theory. Numerical analysis, digital simulations and
experiments for a class of engineering (biological) systems will be
conducted to demonstrate the usefulness and applicability of the obtained
design methods.
Researchers: Z Wang and F Yang (Brunel).
Support:
EPSRC
Duration: 2003-2006
There has been a limited amount of work on the learning of
explanation models directly from multivariate time series (MTS) data.
This type of research is especially important for those applications
where there is a wealth of MTS data but there is no well-established
domain theory or rich body of relevant domain experience, and
where detecting potential problems at an early stage is crucial.
Over the last few years we have researched the issues related to
the learning of such models and made important progress which
calls for further investigation. This project
aims to extend our current
work into a coherent computational framework that is able to produce
reliable and timely explanations from MTS data. This will be achieved
by developing a number of advanced methods for learning efficient and
reliable MTS explanation models, by integrating these methods
into an effective computational framework, and by testing this
framework on a variety of synthetic and real-world MTS, including
DNA microarray data.
Researchers: X Liu and A Tucker
(Brunel).
Support:
EPSRC
Duration: 2002-2004
This interdisciplinary project
brings together four groups of researchers for addressing
challenging issues in understanding virus gene interactions:
the Viral Genomics and Bioinformatics Group at the Windeyer
Institute of Medical Sciences, the Biomolecular Structure and
Modelling Unit at UCL, the Database Technology Group
at Birkbeck College, and the Intelligent Data Analysis (IDA) Group
at Brunel University. It seeks to understand how to determine
the genetic network of molecular interactions using DNA microarray
data. A variety of clustering algorithms and related data pre-processing
techniques will be used, followed by the application of novel short multivariate
time series modelling techniques developed by the IDA Group.
It is expected that the outcome of this research
will help virologists understand better the relationship between the times
when virus genes and host genes are expressed during virus replication,
thereby providing important clues and insights into the virus
disease process.
Researchers: X Liu and S Swift (Brunel),
N Martin (Birkbeck), P Kellam (UCL Virology),
and C Orengo (UCL Biochemistry).
Support: The
BBSRC/EPSRC Bioinformatics Initiative.
Duration: 2001-2005
The proposed project aims to investigate the influence of human factors on the
use of Adaptive Hypermedia Learning Systems (AHLS). Current AHLS mainly focus
on users prior knowledge and ignore other human factors, especially cognitive
styles. We will examine how various hypermedia adaptation techniques are
experienced by users with different cognitive styles. The project will
integrate four aspects of research activities: (1) theoretical evaluation: to
investigate and integrate existing hypermedia adaptation techniques, (2) system
design: to develop an adaptive hypermedia system that can match with particular
preferences of different cognitive styles, (3) empirical study: to examine the
effects of different cognitive styles on learning strategies in the adaptive
hypermedia system, and (4) development of guidelines: to provide guidelines
about the circumstances under which particular adaptive techniques are most
effective. The project can enhance the understanding of human factors in the
use of adaptive hypermedia systems and help designers to develop adaptive
hypermedia systems that can accommodate individual differences.
Researchers: S Chen, T Mitchell, X Liu
and R Macredie (Brunel).
Support:
EPSRC
Duration: 2001-2004
Bioinformatics techniques are powerful tools in assigning function
to newly-identified proteins, but have not yet been widely used
in virological research. We will create a virus database containing
data on virus sequences, structures and derived secondary data
such as conserved sequence motifs. This will provide a platform
for complex analysis of viral protein families. Cross genome
analysis and data mining techniques will be developed to identify
novel relationships between the virus data and to assign functional
information to viral families. In addition, existing search strategies
based on conserved virus protein motifs will be adapted to interrogate
Expression Sequence Tagged (EST) databases to identify new viral proteins
associated with human diseases.
Researchers:
P Kellam and M Mar Alba (UCL Virology),
C Orengo (UCL Biochemistry), X Liu (Brunel) and N Martin (Birkbeck).
Support: The BBSRC/EPSRC
Bioinformatics Initiative.
Duration: 1999-2003
Many statistical Multivariate Time Series (MTS) modelling methods
place constraints on the minimum number of observations in the dataset,
and require distribution assumptions to be made regarding the observed
time series, e.g. the maximum likelihood method for parameter estimation.
To date, we have developed a fast and approximate method based on
evolutionary programming techniques to locate variables that are
highly correlated within high-dimensional MTS. We have also demonstrated
the promises of automated model order selection and parameter estimation
using genetic algorithms. Specifically the method bypassed the size
restrictions of the statistical methods, made no distribution assumptions,
and also located the order and associated parameters as a whole step.
The proposed research will extend the current work on modelling MTS
data into a coherent methodology for forecasting purposes.
This will be achieved by developing methods for model selection
based on the current variable selection work, by improving the
existing methods for model order selection and parameter estimation,
and by integrating the above into an effective forecasting methodology.
Researchers: X Liu and S Swift
(Brunel).
Support:
EPSRC
Duration: 2000-2001
One of the central objectives for this project is to generate
"causal explanations of events" for mutlivariate time series
data, recognised as especially valuable for process industries. The time
series data used came from BP's Petroleum Refinery at Grangemouth in Scotland.
In reviewing the refinery operating data, process engineers often come
across trends with unexpected characteristics. In many cases, these anomalous
events have a significant adverse economic impact,
whether in terms of reduced yield, excessive equipment stress, or
violation of environmental constraints. The identification of such events
is important, but of greater importance still are adequate causal
explanations of them, which could then be used to modify
operating practices, retrain operators (whose inappropriate actions
might have caused the events), conduct anticipatory planning, etc.
Various approaches to the learning of causal models from
multivariate time-series are being investigated and
early results obtained are very encouraging.
Researchers: X Liu and
A Tucker (Brunel), A Ogden-Swift & A Trenchard
(Honeywell Hi Spec Solutions, UK),
S Harp, K Lakshminarayan & T Samad (
Honeywell Technology Center, USA) and
D Campbell-Brown & B Tookey (BP-Amoco).
Support: EPSRC CASE Award;
Honeywell Hi Spec Solutions; BP-Amoco,
Honeywell
Duration: 1997-2000.
The task of identifying haemoglobins is a complex one, requiring
considerable domain knowledge (general biochemistry knowledge
about proteins and haemoglobins, various haemoglobinopathy tests, etc)
and effective analysis of different types of data.
These data include those from isoelectric focusing and electrophoresis,
HPLC (high performance liquid chromatography), mass spectrography,
the DNA structural analysis, tests about oxygen affinity and stability
of haemoglogin, and those about the patient.
Work that has been achieved so far includes data cleaning, data imputation,
the implementation of a haemoglobin database, a system which emulates
the identification procedures followed by the experts,
and early encouraging identification results.
We have plans for obtaining further competitive diagnostic results, for making
the identification process faster and more economical, and for developing
software to assist laboratory scientists.
Researchers: X Liu and S Jami,
with G Loizou (Birkbeck) and S C Davies & J S Heathorn
(Central Middlesex Hospital, London),
F Galacteros & H Wajcman (Henri Mondor Hospital, Paris).
Support: EPSRC CASE Award;
North Thames Regional Health Authority.
Duration: 1995-2000.
Visual field testing provides the eye care practitioner with
essential information regarding the early
detection of major blindness-causing diseases such as glaucoma.
Testing of the visual field is normally performed using an
expensive, specially designed instrument whose use is
mostly restricted to eye hospitals.
However, it is of great importance that visual field testing
be offered to subjects at the earliest
possible stage. By the time a patient has displayed overt symptoms
and has been referred to a hospital for eye examination, it is possible
that the visual field loss is already at an advanced stage and
cannot be easily treated. To remedy the situation, we have exploited
personal computers (PCs) as an affordable test machine.
In particular, a software-based test system has been developed using
machine learning methods (e.g. neural networks and
decision tree induction), an intelligent user interface and
a pattern discovery model, and this system has been used
in several primary care settings, including prevention of optic neuritis
in Africa and opportunistic detection of
glaucoma in a general practice.
Researchers: X Liu and G Cheng,
with G Loizou and K Cho (Birkbeck), and J Wu, B Jones,
R Wormald, F Fitzke and
R Hitchings
(Moorfields Eye Hospital /
Institute of Ophthalmology, London).
Support: British Council for
Prevention of Blindness; MRC,
Taiwan Postgraduate Award;
United Nations Development Programme.
Duration: 1993-
This project aims to develop an intelligent data analysis (IDA)
system for normal tension glaucoma management.
The capabilities of the system will include a centralised
glaucoma management database, intelligent data analysis functions implemented
using modern computational techniques such as
genetic algorithms and statistical pattern recognition, and specific decision
support tools implemented using those IDA functions and expertise
of clinicians. We hope that the use of this system
will assist clinicians in carrying out various aspects of their work
and ultimately improve the glaucoma service in eye hospitals.
Researchers: X Liu and
S Swift, with J Wu, R Hitchings, R Wormald, F Fitzke
(Moorfields Eye Hospital / Institute of Ophthalmology, London).
Support: EPSRC CASE Award;
Moorfields Eye Hospital.
Duration: 1996-2000
Outliers are difficult to handle because some of them can
be measurement or recording errors, while others may represent
phenomena of interest, something significant from the viewpoint of the
application domain. We have so far suggested two ways of distinguishing
between phenomena of interest and measurement noise. The first
attempts to model "real measurements", namely how measurements
should be distributed in a
domain of interest, and rejects values that do not fall within
the real measurements. The other uses knowledge regarding our
understanding of noisy data points instead, so as to help reason about
outliers. Noisy data points are modelled, and those outliers
are accepted if they are not accounted for by a noise model.
New approaches are also being investigated.
Researchers: X Liu and G Cheng,
with J Wu (Moorfields Eye Hospital).
Support: British Council
for Prevention of Blindness; Moorfields Eye Hospital.
Duration: 1994-
Correlating mass spectral data with proposed chemical
structures is the major task of a large number of mass
spectrometry groups around the world.
This correlation task requires considerable human expertise and
competent spectral data analysis.
The SCAMS system has been developed to provide effective computer assistance
for this complex and time-consuming task.
The successful pre-processing of data, before they
are used for correlation, is the most important and challenging
task in SCAMS and consumed most of the project's resources.
Data dimensionality reduction, feature selection, and data quality
assurance are among the key pre-processing activities. To
this end, a variety of domain knowledge is utilised and
computationally intelligent techniques are applied, which include
knowledge based systems, learning meta-knowledge from an industrial
database, and advanced statistical methods. These efforts
have led to the acquisition of most significant and representative
data for neural networks to achieve the correlation task.
Researchers:
R G Johnson, K Mannock, J Phalp and H Dettmar (Birkbeck), X Liu (Brunel),
A Payne (Kodak Research Division,
Harrow) and J Batt (VG BioTech, Fisons Plc, Manchester).
Support: EPSRC, Department of Trade and Industry,
Kodak.
Duration: 1993-1995
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