About
In making advances within Computational Systems Biology there is an acknowledged need for the ongoing development of both probabilistic and mechanistic, possibly multi-scale, models of complex biological processes. In addition to such models the development of appropriate and efficient inferential methodology to identify and reason over such models is necessary. Examples of the progress which has been made in our understanding of modern biology by the exploitation of such methodology include model based inference of p53 activity; uncovering the evolution of protein complexes and understanding the circadian clock in plants; details of which were presented at the LICSB workshops.
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Uploaded videos:
Welcome
Introduction
Apr 17, 2008
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3187 Views
Session 1
Gaussian process modelling of transcription factor networks using Markov Chain M...
Apr 17, 2008
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4501 Views
Data variability could be your friend
Apr 17, 2008
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5478 Views
Time delay analysis
Apr 17, 2008
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5651 Views
Gaussian process modelling of latent chemical species: Applications to inferring...
Apr 17, 2008
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3127 Views
Session 2
Learning Bayesian networks from postgenomic data with an improved structure MCMC...
Apr 17, 2008
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5941 Views
Statistical learning of biological networks: a brief overview
Apr 17, 2008
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4479 Views
Validating inferred gene networks using ODE models of regulation dynamics
Apr 17, 2008
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4166 Views
Relationship between structure and dynamics of gene regulatory networks
Apr 17, 2008
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5518 Views
Session 3
Parameter estimation using moment-closure methods
Apr 17, 2008
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4348 Views
Parameter estimation in biochemical reaction networks: An observer-based approac...
Apr 17, 2008
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3840 Views
BioBayes: Bayesian inference for Systems Biology
Apr 17, 2008
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419122 Views
Session 4
Factor models for QTL studies
Apr 17, 2008
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5389 Views
Probabilistic multi-class multi-kernel learning: On protein fold recognition and...
Apr 17, 2008
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4128 Views
Predicting anti-cancer molecule activity using machine learning algorithms
Apr 17, 2008
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419338 Views