Learning and Inference in Computational Systems Biology (LICSB), Glasgow 2008

Learning and Inference in Computational Systems Biology (LICSB), Glasgow 2008

15 Lectures · Mar 26, 2008

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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Welcome

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02:21

Introduction

Neil D. Lawrence

Apr 17, 2008

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3187 Views

Introduction

Session 1

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20:09

Gaussian process modelling of transcription factor networks using Markov Chain M...

Michalis K. Titsias

Apr 17, 2008

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4502 Views

Lecture
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20:10

Data variability could be your friend

Martino Barenco

Apr 17, 2008

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5478 Views

Lecture
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18:21

Time delay analysis

Catherine Higham

Apr 17, 2008

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5652 Views

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30:15

Gaussian process modelling of latent chemical species: Applications to inferring...

Pei Gao

Apr 17, 2008

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3127 Views

Lecture

Session 2

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28:29

Learning Bayesian networks from postgenomic data with an improved structure MCMC...

Dirk Husmeier

Apr 17, 2008

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5943 Views

Lecture
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25:53

Statistical learning of biological networks: a brief overview

Florence d'Alche-Buc

Apr 17, 2008

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4479 Views

Lecture
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20:31

Validating inferred gene networks using ODE models of regulation dynamics

Michael Stumpf

Apr 17, 2008

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4166 Views

Lecture
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27:44

Relationship between structure and dynamics of gene regulatory networks

Tapesh Santra

Apr 17, 2008

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5518 Views

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Session 3

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21:42

Parameter estimation using moment-closure methods

Colin Gillespie

Apr 17, 2008

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4349 Views

Lecture
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16:44

Parameter estimation in biochemical reaction networks: An observer-based approac...

Eric Bullinger

Apr 17, 2008

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3841 Views

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19:00

BioBayes: Bayesian inference for Systems Biology

Vladislav Vyshemirsky

Apr 17, 2008

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419124 Views

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Session 4

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20:47

Factor models for QTL studies

Oliver Stegle

Apr 17, 2008

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5389 Views

Lecture
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19:34

Probabilistic multi-class multi-kernel learning: On protein fold recognition and...

Theodoros Damoulas

Apr 17, 2008

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4129 Views

Lecture
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16:49

Predicting anti-cancer molecule activity using machine learning algorithms

Jose Santos

Apr 17, 2008

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419339 Views

Lecture