Keynote Speakers

Prof. Anna Goldenberg, SickKids Research Institute and Department of Computer Science at University of Toronto, Toronto, Canada.

Prof. Julio Saez-Rodriguez, Joint Research Center for Computational Biolmedicine, RWTH-Aachen University, Germany and Visting Group Leader at EMBL-EBI, Cambridge, UK.

Prof. Fabian Theis, Institute of Computational Biology, Helmholtz Center Munich, Germany.

Tentative program

Still under construction.

08:30 – 08:40 Opening remarks
Computational methods for emerging biotechnologies
08:40 – 09:30 Invited talk: Lineage estimation from single-cell RNAseq time-series – Fabian J. Theis.
Abstract
09:30 – 10:00 Coffee break + posters
10:00 – 10:25 Contributed talk: Transcriptome-wide splicing quantification in single cells. Yuanhua Huang and Guido Sanguinetti.
10:25 – 10:50 Contributed talk: Gaussian processes for identifying branching dynamics in single cell data. Alexis Boukouvalas, James Hensman and Magnus Rattray.
New advances in Machine Learning for Systems Biology I
10:50 – 11:40 Invited talk: Data integration in computational biology and medicine: current progress and future directions – Anna Goldenberg.
Abstract
11:40 – 12:05 Contributed talk: Modeling post-treatment gene expression change with a deep generative model. Ladislav Rampášek, Daniel Hidru, Peter Smirnov, Benjamin Haibe-Kains and Anna Goldenberg.
12:05 – 14:15 Lunch + posters
New advances in Machine Learning for Systems Biology II
14:15 – 14:40 Contributed talk: Generative learning of dynamic structures using spanning arborescence sets. Anthony Coutant and Celine Rouveirol.
Medical applications
14:40 – 15:30 Invited talk: Understanding and predicting drug efficacy in cancer: from machine learning to biochemical models. Julio Saez-Rodriguez.
Abstract
15:30 – 15:55 Contributed talk: Kernelized rank learning for personalized drug recommendation. Xiao He, Lukas Folkman and Karsten Borgwardt.
15:55 – 16:20 Contributed talk: Ask the doctor - Improving drug sensitivity predictions through active expert knowledge elicitation. Iiris Sundin, Tomi Peltola, Muntasir Mamun Majumder, Pedram Daee, Marta Soare, Homayun Afrabandpey, Caroline Heckman, Samuel Kaski and Pekka Marttinen.
16:20 – 16:30 Closing remarks

List of accepted posters

  • Pioneering topological methods for network-based drug-target prediction by exploiting a brain-network self-organization theory. Claudio Durán, Simone Daminelli, Josephine Thomas, Joachim Haupt, Michael Schroeder and Carlo Vittorio Cannistraci.
  • Kernelized rank learning for personalized drug recommendation. Xiao He, Lukas Folkman and Karsten Borgwardt.
  • MEMNAR: Finding mutually exclusive mutation sets through negative association rule mining. Iman Deznabi, Ahmet Alparslan Celik and Oznur Tastan.
  • Enlightening discriminative network functional modules behind Principal Component Analysis separation in differential-omic science studies. Sara Ciucci, Yan Ge, Claudio Duran, Alessandra Palladini, Víctor Jiménez Jiménez, Luisa María Martínez Sánchez, Yuting Wang, Susanne Sales, Andrej Shevchenko, Steven W. Poser, Maik Herbig, Oliver Otto, Andreas Androutsellis-Theotokis, Jochen Guck, Mathias J. Gerl and Carlo Vittorio Cannistraci.
  • A scalable algorithm for calibrating mathematical models of biochemical pathways using steady state perturbation response data. Tapesh Santra.
  • Ask the doctor - Improving drug sensitivity predictions through active expert knowledge elicitation. Iiris Sundin, Tomi Peltola, Muntasir Mamun Majumder, Pedram Daee, Marta Soare, Homayun Afrabandpey, Caroline Heckman, Samuel Kaski and Pekka Marttinen.
  • PAC Learning of Thomas Regulatory Networks from Time-Series Data. Arthur Carcano, François Fages, Jérémy Grignard and Sylvain Soliman.
  • Integrative concurrent analysis of multiple biological datasets by HALS-based multi-relational NMF. Oliver Mueller-Stricker and Lars Kaderali.
  • Robustness of modeling-based experiment retrieval to differences in measurement and preprocessing techniques. Pradeep Eranti, Paul Blomstedt and Samuel Kaski.
  • Partially ordered expression features improves survival prediction in cancer. Mustafa Buyukozkan, Halil Ibrahim Kuru and Oznur Tastan.
  • Fast imputation of summary statistics based on local LD structure. Matteo Togninalli, Damian Roqueiro and Karsten Borgwardt.
  • Gaussian processes for identifying branching dynamics in single cell data. Alexis Boukouvalas, James Hensman and Magnus Rattray.
  • Exploiting the structure of random forests for the detection of epistatic interactions. Corinna Lewis Schmalohr, Jan Großbach, Andreas Beyer and Mathieu Clément-Ziza.
  • Deep50: Web service for multi-task protein-ligand interaction prediction. Adam Arany, Jaak Simm and Yves Moreau.
  • Scaling up probabilistic pseudotime estimation with the GPLVM. Sumon Ahmed, Alexis Boukouvalas and Magnus Rattray.
  • Smart systems for model exploration with application in computational systems biology. Fredrik Wrede and Andreas Hellander.
  • TOPSPIN: a novel algorithm to predict treatment specific survival in cancer. Joske Ubels, Erik Van Beers, Pieter Sonneveld, Martin van Vliet and Jeroen de Ridder.
  • Knowledge-driven graph evolution (KDGE). Federico Tomasi, Margherita Squillario and Annalisa Barla.
  • FastCMH: Genome-wide genetic heterogeneity discovery with categorical covariates. Felipe Llinares-Lopez, Laetitia Papaxanthos, Dean Bodenham, Damian Roqueiro, COPDGene Investigators and Karsten Borgwardt.
  • easyGWAS: A cloud-based platform for comparing the results of genome-wide association studies. Dominik Grimm, Damian Roqueiro, Matteo Togninalli, EasyGWAS Consortium and Karsten Borgwardt.