The complexity and scale of biological data are growing at an explosive rate. Currently, the main bottleneck towards new biological insights is often our ability to interpret existing data, rather than limitations in the availability of data. In recent years, the field of Machine Learning has provided a number of spectacular advances within various biological domains, and the community of researchers applying Machine Learning to address biological problems is rapidly growing. This conference is an attempt to highlight some of the important current developments on the interface between machine learning and biology—with particular focus on the area of biomolecular modelling and relevant machine learning tools.

Confirmed Speakers

Ken Dill

Ken Dill
Distinguished Professor of Physics & Chemistry, Stony Brook University
Director, Laufer Center for Physical & Quantitative Biology

Klaus-Robert Müller

Klaus-Robert Müller
Professor, Technische Universität Berlin
Distinguished professor at Korea University, Seoul
Co-director of the Berlin Big Data Center

Frank Noé

Frank Noé
Professor, Freie Universität Berlin

Jinbo Xu

Jinbo Xu
Professor of Toyota Technological Institute at Chicago
Senior Fellow, Computational Institute at the University of Chicago

Anatole von Lilienfeld

Anatole von Lilienfeld
Professor at Dept. of Chemistry, University of Basel

Debora S. Marks

Debora S. Marks
Assistant professor of Systems Biology, Harvard Medical School

Douglas Theobald

Douglas Theobald
Associate Professor of Biochemistry, Brandeis University

José Miguel Hernández-Lobato

José Miguel Hernández-Lobato
University Lecturer in Machine Learning, Department of Engineering, University of Cambridge

Chloé-Agathe Azencott

Chloé-Agathe Azencott
Faculty Researcher at Mines ParisTech

Michael Golden

Michael Golden
DPhil student at Brasenose College, Oxford

Simon Olsson

Simon Olsson
Alexander von Humboldt postdoctoral fellow, Freie Universität Berlin

John Ingraham

John Ingraham
PhD Student at Harvard University

Closing talk by

Alexander Bard

Alexander Bard
Swedish cyber-philosopher, music producer and highly sought-after international speaker. He has given lectures on the philosophical, sociological and economic implications of the internet revolution since the late 1990s.

Confirmed Local Speakers

Michael Sørensen

Michael Sørensen
Professor and Chair of the Department of Mathematical Sciences, University of Copenhagen

Christian Igel

Christian Igel
Professor, Department of Computer Science, University of Copenhagen

Ole Winther

Ole Winther
Professor at Section for Cognitive Systems, Technical University of Denmark
Group leader of Gene Regulation, Bioinformatics, University of Copenhagen

Morten Mørup

Morten Mørup
Associate Professor at the Section for Cognitive Systems, Technical University of Denmark

Program

Thursday (Nov 9) Friday (Nov 10)
Session 1
Predicting biomolecular structure
Chair: Thomas Hamelryck		 Mini session
Machine Learning tools
Chair: Thomas Hamelryck
8:45-9:00 Opening remarks 9:15-10:00 Christian Igel
Large-scale machine learning without cloud or cluster
9:00-10:00 Ken Dill
The combinatoric challenge of computing protein structures

Session 4
Quantum Chemistry
Chair: Jes Frellsen
10:00-10:30 Coffee 10:00-11:00 Klaus-Robert Müller
Machine learning meets quantum chemistry
10:30-11:15 Jinbo Xu
Protein contact prediction and folding by deep learning		 11:00-11:30 Coffee
11:15-12:00 John Ingraham
Learning the sequence-to-structure map		 11:30-12:15 Anatole von Lilienfeld
Quantum machine learning
12:00-13:00 Lunch 12:15-13:15 Lunch
Session 2
Biological Sequence Analysis
Chair: Wouter Boomsma		 Session 5
Directional Data
Chain: Søren Hauberg
13:00-13:45 Debora Marks
Generative models for genomics		 13:15-13:45 Michael Sørensen
Toroidal diffusions with a view to protein structure
13:45-14:30 Douglas Theobald
Fast Bayesian simultaneous optimization of multiple structural superpositions and alignments		 13:45-14:15 Michael Golden
A generative angular model of protein structure evolution
14:30-15:00 Ole Winther
Deep learning for biological sequence analysis		 14:15-14:45 Morten Mørup
Directional statistics and Bayesian inference for the modelling of functional neuroimaging data
15:00-15:15 Coffee 14:45-15:00 Coffee
Session 3
Chemical design and drug response
Chair: Jes Frellsen		 Session 6
Protein Dynamics
Chair: Wouter Boomsma
15:15-16:00 Chloé-Agathe Azencott
Leveraging task similarity in multi-task learning for drug response prediction		 15:00-15:45 Frank Noé
Learning molecular kinetics
16:00-16:45 Miguel Hernández-Lobato
Grammar variational autoencoder		 15:45-16:30 Simon Olsson
Conditional models of protein dynamics driven by simulation and experiment
16:45-18:45 Poster Session 16:30-16:40 Break
19:00- Conference Dinner
at Nørrebro Bryghus		 16:40-17:10 Closing talk
Alexander Bard
Beyond man and machine—the rise of intelligent systems in a network world
17:10-18:00 Closing reception

Registration

Two types of ticket are available for this conference, see detail below.

Please be aware that availability is limited and the conference may sell out.

Click the button below for registration.

Conference attendance with lunch (early bird)
This includes conference attendance and lunch Thursday and Friday.
Deadline: October 1, 2017 		DKK200
Conference attendance with lunch
This includes conference attendance and lunch Thursday and Friday.
Deadline: November 1, 2017 		DKK300
Conference attendance with conference dinner
This includes conference attendance, lunch Thursday and Friday and conference dinner Thursday evening at Nørrebro Bryghus.
Deadline: October 30, 2017 		DKK700
Register now

Poster session

Abstract for posters session can be submitted to with subject "Abstract for poster" no later than October 15, 2017. The abstract must be submitted as a single PDF file containing 1) a title, 2) a list of authors and 3) an abstract of no more than 250 words.

The poster abstracts will be lightly peer-reviewed and authors will be notified regarding acceptance by the end of October.

Accommodation

If you are visiting from abroad we recommend the following hotels:

Venue

The conference takes place at

Lundbeck Auditorium
Copenhagen Biocenter
Ole Maaløes Vej 5
2100 Copenhagen, Denmark

Lundbeck Auditorium

To get to the venue from the airport take the Metro M2 to "Nørreport Station" and take a bus. Either take Bus 42 or Bus 6A to "Arresøgade (Tagensvej)" and walk the rest of the way. Both Google Maps and local Danish Travel Planner are most helpful.

Posters

The following posters have been accepted for presentation in the poster session:

  1. Eric Schulze, Matthias Stein: Identification of Metastable States in Complex Type-N Glycans by Markov State Modeling.
  2. Michał Świtnicki, Søren Besenbacher: Probability of the occurrence of germline mutation depends on the shape of DNA.
  3. Alireza Kashani: Phylogentic Additive Regression Model.
  4. José Juan Almagro Armenteros, Casper Kaae Sønderby, Søren Kaae Sønderby, Henrik Nielsen, Ole Winther: DeepLoc: prediction of protein subcellular localization using deep learning.
  5. Christian Jorgensen,  Simone Furini,  Carmen Domene: Energetics of Ion Permeation in an Open-Activated TRPV1 Channel.
  6. Sandro Bottaro, Kresten Lindorff-Larsen: Mapping the Universe of RNA Tetraloop Folds.
  7. Andreas Mardt, Luca Pasquali, Hao Wu and Frank Noé: VAMPnets: Deep learning of molecular kinetics.
  8. John Lamb, Arne Elofsson: Hydrophobicity-driven topology prediction.
  9. Marco Salvatore, Per Warholm, Walter Basile, Nanjiang Shu, Arne Elofsson: The SubCons web-server: A user friendly web interface for state-of-the-art subcellular localization prediction.
  10. Tim Hempel, Guillermo Perez-Hernandez, Martin Scherer, Nuria Plattner, Frank Noe: Markov State Models: Estimation and Model Scoring.
  11. Daniel Nilsson, Sandipan Mohanty, Anders Irbäck: Markov modeling of peptide folding in the presence of protein crowders.
  12. Amelie Stein, Maher M. Kassem, Amanda B. Abildgaard, Louise H. Nielsen, Kaare Teilum, Rasmus Hartmann-Petersen, Kresten Lindorff-Larsen: Stability calculations accurately identify many disease-causing mutations.
  13. Rob Arbon, David Glowacki: Metastable states of Aromatic Amine Dehydrogenase (AADH).
  14. Silvia Amabilino, Dr Lars Bratholm, Dr Simon Bennie, Dr David Glowacki: Neural networks to simulate dynamics.
  15. Anders S. Christensen, Felix A. Faber, Bing Huang, Lars A. Bratholm, Alexandre Tkatchenko, Klaus-Robert Müller, O. Anatole von Lilienfeld: QML: A Python Framework for Quantum Machine Learning.
  16. Tone Bengtsen, Viktor L. Holm, Søren R. Midtgaard, Sandro Bottaro, Lise Arleth, Kresten Lindorff-Larsen: A Bayesian/MaxEnt approach to combine experiments and molecular simulations of small lipid nanodiscs.
  17. Kang Li, Susanne Ditlevsen: Efficient inference methods for mixture models with expensive likelihood.
  18. Charles Bouveyron, Pierre Latouche, Pierre-Alexandre Mattei: Bayesian Variable Selection for Globally Sparse Probabilistic PCA.
  19. Mastaneh Torkamani-Azar, Huseyin Ozkan, Sumeyra Kanik, and Mujdat Cetin: Predicting Vigilance Levels using Mode-based EEG and Behavioral Correlates of Sustained Attention to Response Task.
  20. Peter Bjørn Jørgensen, Murat Mesta, Suranjan Shil, Juan Maria García Lastra, Karsten Wedel Jacobsen, Kristian Sommer Thygesen, and Mikkel Nørgaard Schmidt: Screening of New Materials for Polymer Solar Cells with Machine Learning.
  21. Åke Västermark: Simulation of chloride ion in monkey green cone pigment.
  22. Mustapha Carab H. Ahmed, Micha B. A. Kunze, João M. Martins, Kresten Lindorff-Larsen1: Application of a rotamer library approach to predict paramagnetic relaxation enhancement effects from an IDP structural ensemble.
  23. Daniele Granata, Luca Ponzoni, Kresten Lindorff-Larsen, Cristian Micheletti, Vincenzo Carnevale: From Sequence to Function: Coevolving Amino Acids Encode Structural and Dynamical Domains.
  24. Wouter Boomsma, Jes Frellsen: Spherical convolutions and their application in molecular modelling.
  25. Sigurd Friis Truelsen, Mathias Felix Gruber, Scott T. Myers, Per Amstrup Pedersen, Claus Hélix-Nielsen: Photo-Control of Phosphate Binding Proteins for Biomimetic Resource Recovery.

Organizers

The conference is jointly organized by:

For matters regarding the conference, you can contact .

We are grateful for the generous funding from The Dynamical Systems Interdisciplinary Network under The Excellence Programme for Interdisciplinary Research at the University of Copenhagen.