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 Programme
Overview
Detailed
Programme
| BioInformatics
and Computational Biology II (BCBII) |
CHAIR: COLIN REEVES
Time: Tuesday, March 22nd, 14h20-16h00
| BCBII-1 |
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| Title: |
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Pelican – Protein-structure
Alignment using Cellular Automaton models |
| Author(s): |
Deepak Gangadhar |
| Abstract: |
With more than 23000 protein structures deposited
in the Protein Data Bank (PDB) and more structures being discovered
with each passing day, the experimental determination of the
3-dimensional structure of proteins is just the beginning
of a journey in-silico. For a structural biologist, this enormous
surge of structural data carries with it far greater computational
challenges; Compare, align, classify, and categorize them
under families, domains and functionally similar proteins
already discovered. Pelican provides the structural biologist
with a strong and easy technique that that will help him in
facing these challenges. Pelican is a rapid way to align the
backbones of two protein structures using 2-dimensional Cellular
Automaton (CA) models. Breaking down the protein structure
into distance matrices comprising of 5 peptide units, Pelican
uses the differences of these matrices to construct the 2-dimensional
CA grid. Starting from an initial unaligned state, the CA
evolves through several generations according to a defined
set of local rules. As the CA evolves through successive generations,
the emergent patterns made by the live cells are the ones
that contribute to the alignment. Pelican is also an example
of a system exhibiting emergent behavior. Each cell behaves
in a strictly microscopic way, but each individual cell’s
behavior leads to a macroscopic long range behavior exhibited
by the entire system which collectively gives the alignment.
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| BCBII-2 |
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| Title: |
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An Efficient Algorithm for De Novo
Peptide Sequencing |
| Author(s): |
S. Brunetti,
D. Dutta,
S. Liberatori,
E. Mori,
D. Varrazzo |
| Abstract: |
In this paper we propose a new algorithm for
the de novo peptide sequencing problem. This problem reconstructs
a peptide sequence from a given tandem mass spectra data containing
n peaks. We first build a directed acyclic graph G=(V, E)
in O(n log n) time, where v in V such that v is a spectrum
mass ion or that with complementary mass. The solutions of
this problem are then given by the paths in the graph between
two designated vertices. Unlike previous approaches, the proposed
algorithm does not use dynamic programming, and is built in
a progressive fashion using a priority queue, thus obtaining
an improvement over other methods. |
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| BCBII-3 |
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| Title: |
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Emergent Behavior of Interacting
Groups of Communicative Agents |
| Author(s): |
Alexander Bisler |
| Abstract: |
This paper presents a simulation of the behavior
of different species of birds, which share the same habitat,
but manage to use different times of the day to sing their
songs. Therefore, they avoid a vocal competition and improve
the conditions to find a mate. Communicative agents are used
to model the birds and their behavior. A simple set of rules
is used to make the decisions when and how to change the time
for the search for a mate. By incorporating damping and amplifying
feedback loops the collective behavior of each species led
the system to a solution which was favorable to all agents. |
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| BCBII-4 |
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| Title: |
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Integrating binding site predictions
using meta classification |
| Author(s): |
Y. Sun,
M. Robinson,
R. Adams,
P. Kaye,
N. Davey |
| Abstract: |
Currently the best algorithms for transcription factor binding
site prediction are severely limited in accuracy. There is
good reason to believe that predictions from these different
classes of algorithms could be used in conjunction to improve
the quality of predictions. In this paper, we apply single
layer networks and support vector machines on predictions
from 12 key algorithms. Furthermore, we use a ‘window’
of consecutive results for the input vectors in order to contextualise
the neighbouring results. Moreover, we improve the classification
result with the aid of under- and over- sampling techniques.
We find that by integrating 12 base algorithms, support vector
machines and single layer networks can give better binding
site predictions. |
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| BCBII-5 |
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| Title: |
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Reverse engineering gene networks
with artificial neural networks |
| Author(s): |
A.Krishna ,
A.Narayanan ,
E.C.Keedwell |
| Abstract: |
Temporal gene expression data is of particular
interest to researchers as it can be used to create regulatory
gene networks. Such gene networks represent the regulatory
relationships between genes over time and provide insight
into how genes up- and down-regulate each other from one time-point
to the next (the Biological Motherboard). Reverse engineering
gene networks from temporal gene expression data is considered
an important step in the study of complex biological systems.
This paper introduces sensitivity analysis of trained perceptrons
to reverse engineer the gene networks from temporal gene expression
data. It is shown that a trained neural network, with pruning
(gene silencing), can also be described as a gene network
with minimal re-interpretation, where the sensitivity between
nodes reflects the probability of one gene affecting another
gene in time. The methodology is known as the Neural Network
System Biology Approach with Gene Silencing Simulations (NNSBAGSS).
The methodology was applied to artificial temporal data and
rat CNS development time-course data. |
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