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Machine Learning in Computational Biology (MLCB) 2012 @ Lake Tahoe, December 7, 2012

A workshop at the Annual Conference on Neural Information Processing Systems (NIPS 2012) @ Lake Tahoe, Nevada, USA, December 7, 2012. Room: Sand Harbor 2, Harrah's


This workshop is part of the NIPS conference. To attend it you need to register through the NIPS registration system.

Workshop Description

The field of computational biology has seen dramatic growth over the past few years, in terms of new available data, new scientific questions, and new challenges for learning and inference. In particular, biological data are 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), cell images, 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. Furthermore, next generation sequencing technologies are yielding terabyte scale data sets that require novel algorithmic solutions.

The goal of this workshop is to present emerging problems and machine learning techniques in computational biology. 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. The targeted audience are people with interest in learning and applications to relevant problems from the life sciences.


Program:

7:30-7:40 Introduction and Welcome
7:40-8:00 Structured Domain Adaptation Across Imaging Modality: How 2D Data Helps 3D Inference by G Ratsch et al
8:00-8:20 Peptide Identification of Tandem Mass Spectra via Spectrum Alignment using a Dynamic Bayesian Network by John Halloran, AP Singh, J Bilmes and WS Noble
8:20-8:40 Mining Representative Subgraphs by Substitution Matrices: An Application on Protein Structures by Wajdi Dhifli, R Saidi and EM Nguifo
8:40-9:00 Deep Architectures and Deep Learning for Protein Structure Prediction by Pietro Di Lena, K Nagata and P Baldi
9:00-9:30 Coffee break and Poster setup
9:30-10:20 Invited talk: Lior Pachter, The streaming tree EM algorithm with applications to RNA-Seq
10:20-10:35 Poster Spotlights
10:35-12:00 Poster Session
12:00-15:30 Ski Break
15:30-15:50 SVM2Motif—Reconstructing Overlapping Sequence Motifs by Mimicking an SVM Predictor by Marina Vidovic, M Kloft, N Görnitz, S Sonnenburg, A Zien, KR Müller and G Ratsch
15:50-16:10 Efficient sparse methods for RNA isoforms identification and quantification from RNA-Seq data with network flows by Elsa Bernard, L Jacob, J Mairal and JP Vert
16:10-16:30 Local Diffusion and Cluster Score (LDCS) for Disease Subtyping by Bo Wang and A Goldenberg
16:30-16:50 Mixed forests: non-linear feature selection while accounting for confounding factors by Johannes Stephan, O Stegle and A Beyer
16:50-17:10 PriorNet: a flexible pathway-based model of genetic variation in human disease by Alexis Battle, P Khaitan and D Koller
17:10-17:30 Coffee break
17:30-17:50 Modeling the Clonal Evolution of Cancer from NGS Data by W Jiao, S Vembu, Amit G Deshwar, L Stein and Q Morris
17:50-18:10 Robust and Fast Linear Mixed Models for confounder correction in Genome-wide Association Studies by Cristoph Lippert, J Listgarden and D Heckerman
18:10-18:40 Panel: Machine Learning in CompBio - Looking into the future
18:40-18:45 Closing Remarks by Organizers

 

Submission instructions

Researchers interested in contributing should upload an extended abstract of 4 pages in PDF format to the MLCB submission web site

by Oct 21, 2012, 11:59pm (Samoa time time zone of your choice).

No special style is required. Authors may use the NIPS style file, but are also free to use other styles as long as they use standard font size (11 pt) and margins (1 in).

Submissions should be suitably anonymized and meet the requirements for double-blind reviewing.

All submissions will be anonymously peer reviewed and will be evaluated on the basis of their technical content. A strong submission to the workshop typically presents a new learning method that yields new biological insights, or applies an existing learning method to a new biological problem. However, submissions that improve upon existing methods for solving previously studied problems will also be considered. Examples of research presented in previous years can be found online

The workshop allows submissions of papers that are under review or have been recently published in a conference or a journal. This is done to encourage presentation of mature research projects that are interesting to the community. The authors should clearly state any overlapping published work at time of submission, and should not anonymize their paper in that case.


Invited Speakers


Sponsors


Program Committee

  • Karsten Borgwardt, Max Planck Institute
  • Florence d'Alche-Buc, Université d'Evry-Val d'Essonne, Genopole
  • Alexander Hartemink, Duke University
  • Laurent Jacob, UC Berkeley
  • Neil Lawrence, University of Sheffield
  • Quaid Morris, University of Toronto
  • William Noble, University of Washington
  • Dana Pe'er, Columbia University
  • Yanjun Qi, NEC Labs America
  • Gunnar Rätsch, Sloan-Kettering Institute
  • Alexander Schliep, Rutgers University
  • Oliver Stegle, MPI Tübingen
  • Koji Tsuda, National Institute of Advanced Industrial Science and Technology (Japan)
  • ... and all the organizers (see below)

Organizers



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