3rd International Workshop on Machine Learning in Systems Biology (MLSB), Ljubljana 2009

3rd International Workshop on Machine Learning in Systems Biology (MLSB), Ljubljana 2009

20 Lectures · Sep 5, 2009

About

Molecular biology and all the biomedical sciences are undergoing a true revolution as a result of the emergence and growing impact of a series of new disciplines/tools sharing the “-omics” suffix in their name. These include in particular genomics, transcriptomics, proteomics and metabolomics, devoted respectively to the examination of the entire systems of genes, transcripts, proteins and metabolites present in a given cell or tissue type.

The availability of these new, highly effective tools for biological exploration is dramatically changing the way one performs research in at least two respects. First, the amount of available experimental data is not a limiting factor any more; on the contrary, there is a plethora of it. Given the research question, the challenge has shifted towards identifying the relevant pieces of information and making sense out of it (a “data mining” issue). Second, rather than focus on components in isolation, we can now try to understand how biological systems behave as a result of the integration and interaction between the individual components that one can now monitor simultaneously (so called “systems biology”).

Taking advantage of this wealth of “genomic” information has become a ‘conditio sine qua non’ for whoever ambitions to remain competitive in molecular biology and in the biomedical sciences in general. Machine learning naturally appears as one of the main drivers of progress in this context, where most of the targets of interest deal with complex structured objects: sequences, 2D and 3D structures or interaction networks. At the same time bioinformatics and systems biology have already induced significant new developments of general interest in machine learning, for example in the context of learning with structured data, graph inference, semi-supervised learning, system identification, and novel combinations of optimization and learning algorithms.

The aim of this workshop is to contribute to the cross-fertilization between the research in machine learning methods and their applications to systems biology (i.e., complex biological and medical questions) by bringing together method developers and experimentalists.

The Workshop is organized as "core - event" of Patterns Analysis, Statistical Modelling and Computational Learning - Network of Excellence 2 (PASCAL 2).


More about the workshop can be found at: http://mlsb09.ijs.si/index.html

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Uploaded videos:

Opening

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06:57

Opening

Sašo Džeroski

Oct 05, 2009

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2717 Views

Opening

Invited Talks

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01:02:48

Networking Genes And Drugs: Understanding Gene Function And Drug Mode Of Action ...

Diego di Bernardo

Oct 05, 2009

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4314 Views

Invited Talk
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57:47

Quantitative Microscopy: Bridge Between "Wet" Biology and Computer Science

Yannis L. Kalaidzidis

Oct 05, 2009

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4566 Views

Invited Talk
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01:00:13

Synthetic Biology Achievements And Future Prospects

Roman Jerala

Oct 05, 2009

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6731 Views

Invited Talk
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57:07

Machine Learning Methods For Protein Analyses

William Stafford Noble

Oct 05, 2009

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3974 Views

Invited Talk
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49:43

Metadata For Systems Biology

Nick Juty

Oct 05, 2009

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3532 Views

Invited Talk
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55:34

Automating Science

Ross D. King

Oct 05, 2009

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4689 Views

Invited Talk

Session 1: Networks and Function

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22:39

Predicting the Functions of Proteins in PPI Networks from Global Information

Hossein Rahmani

Oct 05, 2009

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3155 Views

Lecture
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23:44

Integrated Network Construction Using Event Based Text Mining

Yvan Saeys

Oct 05, 2009

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3943 Views

Lecture

Session 2: Biomarkers and Disease

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19:06

On Utility Of Gene Set Signatures In Gene Expression-Based Class Prediction

Minca Mramor

Oct 05, 2009

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6914 Views

Lecture
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31:15

Evaluation Method For Feature Rankings And Their Aggregations For Biomarker Disc...

Ivica Slavkov

Oct 05, 2009

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3307 Views

Lecture

Session 3: Development, Signalling

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21:05

Matching Models to Data in Modelling Morphogen Diffusion

Wei Liu

Oct 05, 2009

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4779 Views

Lecture
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23:45

Evaluation Of Signaling Cascades Based On The Weights From Microarray And ChIP-s...

Zerrin Işık

Oct 05, 2009

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3151 Views

Lecture

Session 4: Machine Learning Methodology

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29:16

A Comparison Of AUC-Estimators In Small-Sample Studies

Antti Airola

Oct 05, 2009

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3356 Views

Lecture
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28:54

Accuracy‐Rejection Curves (ARCs) For Comparison Of Classification Methods With R...

Malik Sajjad Ahmed Nadeem

Oct 05, 2009

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3069 Views

Lecture

Session 5: Function Prediction

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37:58

Hierarchical Cost-Sensitive Algorithms For Genome-Wide Gene Function Prediction

Nicolò Cesa-Bianchi

Oct 05, 2009

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2923 Views

Lecture
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23:29

Simple Ensemble Methods Are Competitive With State-Of-The-Art Data Integration M...

Matteo Re

Oct 05, 2009

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3252 Views

Lecture

Session 6: Phenotype Prediction

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22:13

A Subgroup Discovery Approach For Relating Chemical Structure And Phenotype Data...

Lan Umek

Oct 05, 2009

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3513 Views

Lecture
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27:06

Evaluation Of Methods In Gene Association Studies: Yet Another Case For Bayesia...

Gábor Hullám

Oct 05, 2009

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3938 Views

Lecture

Discussion and Closing Remarks

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04:36

Closing Remarks

Sašo Džeroski

Oct 05, 2009

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2599 Views

Lecture