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Machine Learning in Computational Biology (MLCB) 2013 @ Lake Tahoe, December 10, 2013

A workshop at the Annual Conference on Neural Information Processing Systems (NIPS 2013) @ Lake Tahoe, Nevada, USA, December 10, 2013. Room: Harvey's Zephyr


Important dates

  • Deadline extended: submission due Oct 24, 2013, 11:59pm (time zone of your choice)
  • Submission Due Time: Oct 22, 2013
  • Decision notifications: Nov 4, 2013 (tentative)
  • Workshop: Dec 10, 2013


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.


Invited Speakers

  • Jonathan Pritchard, Stanford (USA)
  • Title: Genetic variation in gene regulation
  • Abstract: Genetic variants that impact gene expression play a central role in the genetics of complex traits and in evolution. Yet the precise links between genetic variation and changes in gene regulation are poorly understood and it remains very difficult to predict which variants have regulatory effects in any given cell type. In this talk I will describe work we have done on identifying genetic variants that impact gene expression and understanding the primary mechanisms by which such variants act.
  • Samuel Kaski, HIIT (Finland)
  • Title: Multi-view multi-task learning for drug sensitivity prediction
  • Abstract:In the core of personalized medicine is the computational task of predicting drug sensitivities based on genomic information. This is a supervised learning task which can be addressed by a combination of multi-view and multi-task learning. Alternatively, it can be viewed as a structured prediction task or a recommender system. I will discuss an approach which has recently turned out to be successful, Bayesian kernelized multi-view multi-task methods for predicting sensitivities across drug profiles, and its generalizations to matrix factorization with side information.

Program

7:30-7:35 Introduction and Welcome
7:35-7:55 Multi-trait genomic selection via multivariate regression with structured regularization, by Julien Chiquet, Stephane Robin and Tristan Mary-Huard[pdf]
7:55-8:15 Epigenome-Wide Association Studies Without the Need for Cell-Type Composition, by James Zou, Christoph Lippert, David Heckerman, Martin Aryee and Jennifer Listgarten
8:15-9:00 Invited talk: Jonathan Pritchard. Genetic variation in gene regulation.
9:00-9:30 Coffee break and Poster setup
9:30-9:50 A Poisson-multivariate normal hierarchical model for measuring microbial conditional independence networks from metagenomic count data, by Surojit Biswas, Derek Lundberg, Jeffery Dangl and Vladimir Jojic
9:50-10:10 Multisource transfer learning for host-pathogen protein interaction prediction in unlabeled tasks, by Meghana Kshirsagar, Jaime Carbonell and Judith Klein-Seetharaman[pdf]
10:10-10:30 Poster Spotlight
10:30-12:00 Poster Session
12:00-15:30 Ski Break
15:35-15:55 Sparse Estimation of Module Gaussian Graphical Models with Applications to Cancer Systems Biology, by Safiye Celik, Benjamin A. Logsdon and Su-In Lee
15:55-16:15 Identifying Perturbed Genes in the Regulatory Networks from Gene Expression Data, by Maxim Grechkin and Su-In Lee.
16:15-17:00 Invited talk: Samuel Kaski. Multi-view multi-task learning for drug sensitivity prediction.
17:00-17:30 Coffee break
17:30-17:50 Kernel bilinear regression for toxicogenetics, by Elsa Bernard, Erwan Scornet, Yunlong Jiao, Véronique Stoven, Thomas Walter and Jean-Philippe Vert
17:50-18:10 A convolutional model of RNA-binding proteins, by Andrew Delong, Babak Alipanahi and Brendan Frey
18:10-18:30 Assessing the Feasibility of Learning Biomedical Phenotypes via Large Scale Omics Profiles, by Mohsen Hajiloo and Russell Greiner
18:30-18:45 Closing Remarks by Organizers

 

Posters

1) Identification of behavioral pathways as a dimensionality reduction problem, by Andrea Censi, Andrew Straw, Richard Murray and Michael H. Dickinson
2) Collective Inference and Multi-Relational Learning for Drug–Target Interaction Prediction, by Shobeir Fakhraei, Bert Huang and Lise Getoor
3) Molecular Fingerprint Prediction with Multiple Kernel Learning, by Huibin Shen, Kai Dührkop, Sebastian Böcker and Juho Rousu
4) Kernelized Bayesian Matrix Factorization, by Mehmet Gönen, Muhammad Ammad-Ud-Din, Suleiman A. Khan and Samuel Kaski
5) Entropic Graph-based Posterior Regularization for Learning Probabilistic Models, by Maxwell Libbrecht, Michael Hoffman, William Noble and Jeffrey Bilmes[pdf]
6) Tissue-Dependent Alternative Splicing Prediction Using Deep Neural Network, by Michael K. K. Leung, Hui Yuan Xiong, Leo J. Lee and Brendan J. Frey
7) Integration of multi-level cellular phenotypic data using automated microscopy and Bayesian networks modeling, by Heba Sailem, Julia Sero and Chris Bakal
8) A fast homotopy algorithm for a large class of weighted classification problems, by Pierre Gutierrez, Guillem Rigaill and Julien Chiquet[pdf]
9) Sequence Classification with Probabilistic Subsequence-based Models, by Sam Blasiak, Huzefa Rangwala and Kathryn Laskey
10) Randomisation of next generation sequencing data while preserving genomic event distributions , by Gobbi Andrea, Francesco Iorio, Kevin Dawson, et al.[pdf]
11) Inference in Stochastic Biological Systems using Gaussian Process Surrogate ABC , by Edward Meeds and Max Welling
12) Sparse discriminative latent characteristics for predicting cancer drug sensitivity , by David Knowles, Alexis Battle and Daphne Koller.
13) MoGDIW, an integrated workflow for cell motility genes discovery in high-throughput time-lapse screening data, by Alice Schoenauer Sebag, Céline Raulet-Tomkiewicz, Robert Barouki, Jean-Philippe Vert and Thomas Walter
14) A Method for Mining Discriminative Graph Patterns, by Andrea Fuksová, Ondřej Kuželka and Andrea Szabóová.[pdf]
15) Modeling context-specific gene regulation with multi-task boosting, by Sofia Kyriazopoulou - Panagiotopoulou, Marco Cusumano-Towner, Serafim Batzoglou and Anshul Kundaje.
16) Positive Random Projections for Gene Expression Data , by Amit Deshwar and Quaid Morris
17) Adaptive Gradient Matching for Chemical Master Equation Parameter Inference , by Stefan Ganscha and Manfred Claassen
18) Feature Selection using One Class SVM: A New Perspective , by Yamuna Prasad, K.K. Biswas and Parag Singla.[pdf]
19) Efficient identification of epistatic effects in multifactorial disorders , by Orlando Anunciação, Susana Vinga and Arlindo Oliveira.[pdf]
20) Interpretable Sparse High-Order Boltzmann Machines for Transcription Factor Interaction Identification , by Martin Renqiang Min, Xia Ning, Chao Cheng and Mark Gerstein

 

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 24, 2013, 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.


Program Committee:

  • Babak Alipanahi, University of Toronto
  • Alexis Battle, Stanford
  • Karsten Borgwardt, Max Planck Institute
  • Gal Chechik, Gonda brain center, Bar Ilan University
  • Florence d'Alche-Buc, Université d'Evry-Val d'Essonne, Genopole
  • CS Foo, Stanford
  • Nicolo Fusi, Sheffield Institute
  • Alexander Hartemink, Duke University
  • Antti Honkela, University of Helsinki
  • Laurent Jacob, UC Berkeley
  • David Knowles, Stanford
  • Christina Leslie , Sloan-Kettering Institute
  • Christoph Lippert, MSR
  • Jennifer Listgarten, MSR
  • John Marioni, EMBL-EBI
  • Klaus-Robert Müller, Fraunhofer FIRST
  • William Noble, University of Washington
  • Nico Pfeifer , Max Planck Institute
  • Yanjun Qi, NEC Labs America
  • Gunnar Rätsch, Sloan-Kettering Institute
  • Magnus Rattray, University of Manchester
  • Karen Sachs, Stanford
  • Alexander Schliep, Rutgers University
  • Koji Tsuda, National Institute of Advanced Industrial Science and Technology (Japan)
  • Bo Wang, Stanford
  • ... and all the organizers (see below)

Organizers



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