NIPS Workshop on Machine Learning in Computational Biology, Whistler 2008

NIPS Workshop on Machine Learning in Computational Biology, Whistler 2008

10 Lectures · Dec 12, 2008

About

The field of computational biology has seen dramatic growth over the past few years, both in terms of new available data, new scientific questions, and new challenges for learning and inference. In particular, biological data is often relationally structured and highly diverse, well-suited to approaches that combine multiple weak evidence from heterogeneous sources. These data may include sequenced genomes of a variety of organisms, gene expression data from multiple technologies, protein expression data, protein sequence and 3D structural data, protein interactions, gene ontology and pathway databases, genetic variation data (such as SNPs), and an enormous amount of textual data in the biological and medical literature. New types of scientific and clinical problems require the development of novel supervised and unsupervised learning methods that can use these growing resources.

The goal of this workshop is to present emerging problems and machine learning techniques in computational biology. We invited several speakers from the biology/bioinformatics community who will present current research problems in bioinformatics, and we invite contributed talks on novel learning approaches in computational biology. We encourage contributions describing either progress on new bioinformatics problems or work on established problems using methods that are substantially different from standard approaches. Kernel methods, graphical models, feature selection and other techniques applied to relevant bioinformatics problems would all be appropriate for the workshop.

More information about the workshop can be found here.

Related categories

Uploaded videos:

video-img
23:01

Learning Temporal Sequence of Biological Networks

Le Song

Dec 20, 2008

 · 

3830 Views

Lecture
video-img
27:44

Switching Regulatory Models of Cellular Stress Response

Guido Sanguinetti

Dec 20, 2008

 · 

3228 Views

Lecture
video-img
24:34

Detecting the Presence and Absence of Causal Relationships Between Expression o...

Eun Yong Kang

Dec 20, 2008

 · 

3617 Views

Lecture
video-img
23:01

KIRMES: Kernel-based Identification of Regulatory Modules in Euchromatic Sequen...

Sebastian J. Schultheiss

Dec 20, 2008

 · 

2728 Views

Lecture
video-img
24:32

Approximate Substructure Matching for Biological Sequence Classification

Vladimir Pavlovic

Dec 20, 2008

 · 

3474 Views

Lecture
video-img
21:44

Predicting Binding Affinities of MHC Class II Epitopes Across Alleles

Nico Pfeifer

Dec 20, 2008

 · 

3884 Views

Lecture
video-img
20:50

Full Bayesian Survival Models for Analyzing Human Breast Tumors

Volker Roth

Dec 20, 2008

 · 

4911 Views

Lecture
video-img
20:58

Probabilistic assignment of formulas to mass peaks in metabolomics experiments

Simon Rogers

Dec 20, 2008

 · 

3019 Views

Lecture
video-img
28:29

Learning “graph-mer” motifs that predict gene expression trajectories in develo...

Christina Leslie

Dec 20, 2008

 · 

3024 Views

Lecture
video-img
25:36

On the relationship between DNA periodicity and local chromatin structure

Sheila M. Reynolds

Dec 20, 2008

 · 

3251 Views

Lecture