program and abstract - Advanced Analytics Institute - University of ...

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1 Οκτ 2013 (πριν από 3 χρόνια και 8 μήνες)

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UTS
AAI Bioinformatics Workshop 201
2


Date a
nd Time
: 12/01/2012, 9:00am to 6
:
0
0pm

Venue
: Building 5, UTS Blackfriars Campus, 2
-
12 Blackfriars Street, Chippendale
,

NSW
2008

(5 minutes walking distance from the UTS Tower Building, City Campus)

Workshop
Facilitator
: Associate Professor Jinyan Li, Advanced Analytics Institute (AAI),
University of Technology Sydney (UTS)

Fees
: Free admission


9:00am


9:05
am
:

Opening address

by AAI’s director, Professor Longbing Cao

9:05
am


9:
15
am
:

Dean’s Welcome
by Prof
Hung Nguyen, Dean of FEIT, UTS


9:1
5am


10:
1
5am
:
Keynote
, A Novel Principle for Childhood ALL Relapse Prediction,
by
Professor Limsoon Wong (H
ead of

Computer Science
, School of Computing
, National
University of Singapore)


10:
1
5am


1
0
:
30
am
:
Tea break


1
0
:
30
am


1
1
:
15
am
: Invited

t
alk
, On Improving the Power of Tests for the Detection of
Differential

Expression via Mixture Models,
by Professor Geoff McLachlan (
Director, Centre
for Statistics
,
The
U
niversity of
Q
ueensland)


11:
15
am


12:
00
pm
:

Invited t
alk
,
Rewiring the D
ynamic
I
nteractome
,
by Professor
Mark
Ragan (ARC Centre of Excellence in Bioinformatics, and Institute for Molecular Bioscience,
The University of Queensland)


12:
00
pm


1:30pm
:

lunch


1:30pm


2:
15
pm
: Invited

talk

by
Professor
Pablo Moscato
(
Co
-
Director
-

Priority Research
Centre for Bioinformatics, Biomarker Discovery and Information
-
based Medicine, The
University of Newcastle
)


2:
15
pm


3:
0
0pm
: Invited

talk
,
Information Discovery for Biomedical Applications
,

by
Professor Phoebe Chen (Chairman,
C
omputer
S
cience
D
ept,
La Trobe

University
)


3:
0
0pm


3:
30
pm
:

T
ea break


3:
30
pm


4:
1
5pm
: Invited talk,

Improving similarity scores of comparing motifs,
by Dr. Uri
Keich (
School of Mathematics and Statistics, The U
niversity of Sydney).


4:15pm


5:00pm
:

Host
talk
, Water Exclusion and Inclusion in the Prediction of Protein

Binding Hotspots,
by
Dr. Jinyan Li (Advanced Analytics Institute, University of Technology,
Sydney)


5
:
00
pm


5
:
1
0pm
: Concluding Remarks by
Professor Mary
-
Anne Williams (Associate Dean,
FEIT, UTS)

to be confirmed


5:10pm


6:00pm: A
AI tour and demo

6:30pm


8:00pm dinner



Abstracts of the Talks


9:15am


10:15am: Keynote, A Novel Principle for Childhood ALL Relapse Prediction, by
Professor
Limsoon Wong (Head of Computer Science, School of Computing, National
University of Singapore)




Abstract:
Childhood acute lymphoblastic leukemia (ALL) is the most common type of
cancer in children. Contemporary management of patients with childhood ALL is
based on the concept of tailoring the intensity of therapy to a patient’s risk of relapse.
However, a significant number of patients with good prognostic characteristics
relapse, while some with poor prognostic features survive. There is thus a demand to
i
mprove relapse prediction. Current treatment of childhood ALL is a process of
gradually removing leukemic cells in a patient. Thus, we hypothesize that a leukemic
sample consists of a mixture of leukemic cells and normal cells, where the intensity of
the l
eukemic genetic signature measured by gene expression profile (GEP) can be
used to infer the proportion of leukemic cells in the sample. In addition, as early
response is known to have a great prognostic value in childhood ALL, we further
expect to perform

relapse prediction by the rate of the reduction of leukemic cells
during treatment. To validate our hypothesis, for the first time, we generate time
-
series GEPs in a leukemia study. We demonstrate that the time
-
series GEPs are
capable of mimicking the rem
oval of leukemic cells in patients during disease
treatment. We further propose to predict relapse based on the change of GEPs
between different time points
---
the genetic status shifting (GSS) model. Our results
suggest the prognostic strength of GSS is su
perior to that of any other prognostic
factors of childhood ALL. In our study, e.g., GSS outperforms MRD by over 20% in
the accuracy of relapse prediction. (* This talk is based on the dissertation of my
student Dr Dong Difeng. *)



10:30am


11:15am:
Invited talk, On Improving the Power of Tests for the Detection of
Differential Expression via Mixture Models, by Professor Geoff McLachlan (Director, Centre
for Statistics, The University of Queensland)




Abstract: There are many important problems that re
quire carrying out hundreds or
even thousands of hypothesis testing problems at the same time. For example, the first
and often major goal of microarray experiments is the detection of differentially
expressed genes in a given number of classes. More recen
tly, consideration has been
given to the clustering of gene profiles in order to improve the power in detecting
differentially expressed genes in experiments of small sample size. The primary goal
is to cluster gene profiles in order to ascertain or share

information about the state of
the gene. We use a mixture of linear models to effect the clustering of gene profiles
into a number of tight clusters. From these clusters we are able to form a pooled
estimate of the variance of a gene to provide a test sta
tistic with improved power for
the detection of differential expression. We demonstrate this approach on a number
of real data sets from the bioinformatics literature.


11:15am


12:00pm: Invited talk, Rewiring the Dynamic Interactome, by Professor Mark
R
agan (ARC Centre of Excellence in Bioinformatics, and Institute for Molecular Bioscience,
The University of Queensland)




Abstract: Inferring and analysing networks of biomolecular

interaction in the
mammalian cell represents one of the main themes of research in my group. Within
this theme we focus on networks of protein
-
protein interaction and of gene regulation.
In this presentation I will describe our recent work characterising,

at whole
-
transcriptome scale, how the generation of alternative transcripts rewires the protein
interactome in human and can lead to functional diversity including chronic disease.
Our results highlight the different roles potentially played by protein is
oforms, and the
importance of retaining isoform
-
level resolution in the construction of interactome
maps. I will also briefly present ongoing work on the inference of
gene regulatory
networks (GRNs) from high
-
throughput microarray expression data, both syn
thetic
and from empirical data in human
serous papillary ovarian adenocarcinoma
. We
uncover
cross
-
regulation of angiogenesis
-
specific genes through three key
transcription factors in normal and cancer conditions, and propose testable
mechanistic models lin
king gene regulation to cancer.


1:30pm


2:15pm: Invited talk by Professor Pablo Moscato (
Co
-
Director
-

Priority Research
Centre for Bioinformatics, Biomarker Discovery and Information
-
based Medicine, The
University of Newcastle
)




Abstract: TBA.


2:15pm


3:00pm: Invited talk, Information Discovery for Biomedical Applications, by
Professor Phoebe Chen (Chairman, Computer Science Dept, La Trobe University)




Abstract: In this talk, I will demonstrate recent methodologies and data structures for
gathering hi
gh
-
quality approximations and modelling of genomic information, and
will use these innovations as the basis for developing methods to cluster and visualize
biomedical data in information discovery.


3:30pm


4:15pm: Invited talk, Improving similarity score
s of comparing motifs, by Dr. Uri
Keich (School of Mathematics and Statistics, The University of Sydney).




Abstract:
A question that often comes up after applying a motif finder to a set of co
-
regulated DNA sequences is whether the reported putative motif
is similar to any
known motif. While several tools have been designed for this task, Habib et al. (2008)
pointed out that scores that are commonly used for measuring similarity between
motifs do not distinguish between a good alignment of two informative c
olumns and
one of two uninformative columns. This observation explains why motif database
tools such as TOMTOM occasionally return an alignment of uninformative columns
which is clearly spurious. We offer a general approach to adjust any motif similarity
s
core so as to reduce the number of reported spurious alignments of uninformative
columns. Our method is implemented as the default scoring scheme in the latest
release of TomTom.


4:15pm


5:00pm: Host talk, Water Exclusion and Inclusion in the Prediction
of Protein
Binding Hotspots, by Dr. Jinyan Li (Advanced Analytics Institute, University of Technology,
Sydney)




Abstract: I will give an introduction to the long
-
standing O
-
ring theory and a refined
version called double water exclusion for modelling prote
in binding sites.

I also
describe a tripartite model for computational implementation.

This protein
-
water
-
protein tripartite model is novel as immobilized water molecules are teamed in this
graph model. I will
illustrate

how this model is used to predict p
rotein binding
hotspots in binding interfaces, and why the mutation of H1N1 2009 did not cause a
bigger pandemic disaster than the Spanish Flu pandemic in 1918.