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ExCAPE

Exascale Compound Activity Prediction Engine

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EC-Contrib. €

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Partnership

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Project "ExCAPE" data sheet

The following table provides information about the project.

Coordinator
INTERUNIVERSITAIR MICRO-ELECTRONICA CENTRUM 

Organization address
address: KAPELDREEF 75
city: LEUVEN
postcode: 3001
website: www.imec.be

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Belgium [BE]
 Project website http://excape-h2020.eu/
 Total cost 3˙910˙140 €
 EC max contribution 3˙910˙140 € (100%)
 Programme 1. H2020-EU.1.2.2. (FET Proactive)
 Code Call H2020-FETHPC-2014
 Funding Scheme RIA
 Starting year 2015
 Duration (year-month-day) from 2015-09-01   to  2018-08-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    INTERUNIVERSITAIR MICRO-ELECTRONICA CENTRUM BE (LEUVEN) coordinator 873˙146.00
2    VYSOKA SKOLA BANSKA - TECHNICKA UNIVERZITA OSTRAVA CZ (OSTRAVA PORUBA) participant 532˙500.00
3    INTEL CORPORATION BE (KONTICH) participant 507˙500.00
4    JANSSEN CILAG SA ES (Madrid) participant 448˙707.00
5    ASTRAZENECA AB SE (SODERTAELJE) participant 403˙125.00
6    UNIVERSITAT LINZ AT (LINZ) participant 371˙623.00
7    ROYAL HOLLOWAY AND BEDFORD NEW COLLEGE UK (EGHAM) participant 369˙943.00
8    AALTO KORKEAKOULUSAATIO SR FI (ESPOO) participant 283˙593.00
9    IDEACONSULT LIMITED LIABILITY COMPANY BG (SOFIA) participant 120˙000.00

Map

 Project objective

Scalable machine learning of complex models on extreme data will be an important industrial application of exascale computers. In this project, we take the example of predicting compound bioactivity for the pharmaceutical industry, an important sector for Europe for employment, income, and solving the problems of an ageing society. Small scale approaches to machine learning have already been trialed and show great promise to reduce empirical testing costs by acting as a virtual screen to filter out tests unlikely to work. However, it is not yet possible to use all available data to make the best possible models, as algorithms (and their implementations) capable of learning the best models do not scale to such sizes and heterogeneity of input data. There are also further challenges including imbalanced data, confidence estimation, data standards model quality and feature diversity.

The ExCAPE project aims to solve these problems by producing state of the art scalable algorithms and implementations thereof suitable for running on future Exascale machines. These approaches will scale programs for complex pharmaceutical workloads to input data sets at industry scale. The programs will be targeted at exascale platforms by using a mix of HPC programming techniques, advanced platform simulation for tuning and and suitable accelerators.

 Deliverables

List of deliverables.
Final simulation and scalability report (4 and 2) Documents, reports 2019-07-25 13:11:11
Development report Documents, reports 2019-07-25 13:11:12
Report + Code 6 Documents, reports 2019-07-25 13:11:12
Metamodel report Documents, reports 2019-07-25 13:11:11
Simulation report 2 Documents, reports 2019-07-25 13:11:11
Tox Other 2019-07-25 13:11:11
Simulation Report 1 Documents, reports 2019-07-25 13:11:11
WebData Other 2019-07-25 13:11:12
Workflows report Documents, reports 2019-07-25 13:11:11
Report + Code 4 Other 2019-07-25 13:11:12
Factsheet 1 Documents, reports 2019-07-25 13:11:11
PublicBio Other 2019-07-25 13:11:11
PublicCancer Other 2019-07-25 13:11:11
Workshop 2 Demonstrators, pilots, prototypes 2019-07-25 13:11:12
Website Websites, patent fillings, videos etc. 2019-07-25 13:11:11
Factsheet 2 Documents, reports 2019-07-25 13:11:12
Scalability report 1 Documents, reports 2019-07-25 13:11:11
Challenge report Documents, reports 2019-07-25 13:11:11
Social Websites, patent fillings, videos etc. 2019-07-25 13:11:12
Criteria report Documents, reports 2019-07-25 13:11:11
Simulation report 3 Documents, reports 2019-07-25 13:11:11

Take a look to the deliverables list in detail:  detailed list of ExCAPE deliverables.

 Publications

year authors and title journal last update
List of publications.
2015 Djork-Arne Clevert, Thomas Unterthiner, Sepp Hochreiter
Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
published pages: 1-14, ISSN: , DOI:
CoRR abs/1511.07289 2019-07-25
2016 Vladimir Vovk, Dusko Pavlovic
Universal Probability-Free Conformal Prediction
published pages: 40-47, ISSN: , DOI: 10.1007/978-3-319-33395-3_3
COPA 2016: Conformal and Probabilistic Prediction with Applications 2019-07-25
2016 Vladimir Vovk, Valentina Fedorova, Ilia Nouretdinov, Alexander Gammerman
Criteria of Efficiency for Conformal Prediction
published pages: 23-39, ISSN: , DOI: 10.1007/978-3-319-33395-3_2
COPA 2016: Conformal and Probabilistic Prediction with Applications 2019-07-25
2015 Valentina Fedorova, Alex Gammerman, Ilia Nouretdinov, Vladimir Vovk
Hypergraphical Conformal Predictors
published pages: 1560003, ISSN: 0218-2130, DOI: 10.1142/S0218213015600039
International Journal on Artificial Intelligence Tools 24/06 2019-07-25
2017 Jiangming Sun, Nina Jeliazkova, Vladimir Chupakin, Jose-Felipe Golib-Dzib, Ola Engkvist, Lars Carlsson, Jörg Wegner, Hugo Ceulemans, Ivan Georgiev, Vedrin Jeliazkov, Nikolay Kochev, Thomas J. Ashby, Hongming Chen
ExCAPE-DB: an integrated large scale dataset facilitating Big Data analysis in chemogenomics
published pages: , ISSN: 1758-2946, DOI: 10.1186/s13321-017-0203-5
Journal of Cheminformatics 9/1 2019-07-25
2016 Michael Treml, José Arjona-Medina, Thomas Unterthiner, Rupesh Durgesh, Felix Friedmann, Peter Schuberth, Andreas Mayr, Martin Heusel, Markus Hofmarcher, Michael Widrich, Bernhard Nessler, Sepp Hochreiter
Speeding up Semantic Segmentation for Autonomous Driving
published pages: 1-7, ISSN: , DOI:
OpenReview 2019-07-25
2016 Paolo Toccaceli, Ilia Nouretdinov, Alexander Gammerman
Conformal Predictors for Compound Activity Prediction
published pages: 51-66, ISSN: , DOI: 10.1007/978-3-319-33395-3_4
COPA 2016: Conformal and Probabilistic Prediction with Applications 2019-07-25
2017 Ilia Nouretdinov
Validity and efficiency of conformal anomaly detection on big distributed data
published pages: 254-267, ISSN: 2415-6698, DOI: 10.25046/aj020335
Advances in Science, Technology and Engineering Systems Journal 2/3 2019-07-25
2017 V. Vovk
Purely pathwise probability-free Ito integral
published pages: , ISSN: 1027-4634, DOI: 10.15330/ms.46.1.96-110
Matematychni Studii 46/1 2019-07-25
2017 Imen Chakroun, Tom Haber, Thomas J. Ashby
SW-SGD: The Sliding Window Stochastic Gradient Descent Algorithm
published pages: 2318-2322, ISSN: 1877-0509, DOI: 10.1016/j.procs.2017.05.082
Procedia Computer Science 108 2019-07-25
2017 Balazs Nemeth, Tom Haber, Thomas J. Ashby, Wim Lamotte
Improving Operational Intensity in Data Bound Markov Chain Monte Carlo
published pages: 2348-2352, ISSN: 1877-0509, DOI: 10.1016/j.procs.2017.05.024
Procedia Computer Science 108 2019-07-25
2018 Noé Sturm, Jiangming Sun, Yves Vandriessche, Andreas Mayr, Günter Klambauer, Lars-Anders Carlson, Ola Engkvist, Hongming Chen
Application of Bioactivity Profile Based Fingerprints for Building Machine Learning Models
published pages: , ISSN: 2573-2293, DOI: 10.26434/chemrxiv.6969584.v1
ChemRxiv 2019-07-25
2017 Tom Vander Aa, Imen Chakroun, Tom Haber
Distributed Bayesian Probabilistic Matrix Factorization
published pages: 1030-1039, ISSN: 1877-0509, DOI: 10.1016/j.procs.2017.05.009
Procedia Computer Science 108 2019-07-25
2017 Paolo Toccaceli, Ilia Nouretdinov, Alexander Gammerman
Conformal prediction of biological activity of chemical compounds
published pages: 105-123, ISSN: 1012-2443, DOI: 10.1007/s10472-017-9556-8
Annals of Mathematics and Artificial Intelligence 81/1-2 2019-07-25
2017 Vladimir Vovk
The role of measurability in game-theoretic probability
published pages: 719-739, ISSN: 0949-2984, DOI: 10.1007/s00780-017-0336-4
Finance and Stochastics 21/3 2019-07-25
2018 Andreas Mayr, Günter Klambauer, Thomas Unterthiner, Marvin Steijaert, Jörg K. Wegner, Hugo Ceulemans, Djork-Arné Clevert, Sepp Hochreiter
Large-scale comparison of machine learning methods for drug target prediction on ChEMBL
published pages: 5441-5451, ISSN: 2041-6520, DOI: 10.1039/c8sc00148k
Chemical Science 9/24 2019-07-25
2018 Vladimir Vovk, Jieli Shen, Valery Manokhin, Min-ge Xie
Nonparametric predictive distributions based on conformal prediction
published pages: , ISSN: 0885-6125, DOI: 10.1007/s10994-018-5755-8
Machine Learning 2019-07-25
2017 Gundula Povysil, Antigoni Tzika, Julia Vogt, Verena Haunschmid, Ludwine Messiaen, Johannes Zschocke, Günter Klambauer, Sepp Hochreiter, Katharina Wimmer
panelcn.MOPS: Copy-number detection in targeted NGS panel data for clinical diagnostics
published pages: 889-897, ISSN: 1059-7794, DOI: 10.1002/humu.23237
Human Mutation 38/7 2019-07-25
2017 Vladimir Vovk, Ilia Nouretdinov, Valentina Fedorova, Ivan Petej, Alex Gammerman
Criteria of efficiency for set-valued classification
published pages: 21-46, ISSN: 1012-2443, DOI: 10.1007/s10472-017-9540-3
Annals of Mathematics and Artificial Intelligence 81/1-2 2019-07-25
2018 Alberto Scionti, Somnath Mazumdar, Antoni Portero
Towards a Scalable Software Defined Network-on-Chip for Next Generation Cloud
published pages: 2330, ISSN: 1424-8220, DOI: 10.3390/s18072330
Sensors 18/7 2019-07-25
2018 Paolo Toccaceli, Alexander Gammerman
Combination of inductive mondrian conformal predictors
published pages: , ISSN: 0885-6125, DOI: 10.1007/s10994-018-5754-9
Machine Learning 2019-07-25
2017 Vladimir Vovk, Dusko Pavlovic
Universal probability-free prediction
published pages: 47-70, ISSN: 1012-2443, DOI: 10.1007/s10472-017-9547-9
Annals of Mathematics and Artificial Intelligence 81/1-2 2019-07-25
2018 Nikolay Kochev, Svetlana Avramova, Nina Jeliazkova
Ambit-SMIRKS: a software module for reaction representation, reaction search and structure transformation
published pages: , ISSN: 1758-2946, DOI: 10.1186/s13321-018-0295-6
Journal of Cheminformatics 10/1 2019-07-25
2017 Eemeli Leppäaho, Muhammad Ammad-ud-din, Samuel Kaski
Exploratory Analysis of Multiple Data Sources with Group Factor
published pages: 1-5, ISSN: 1533-7928, DOI:
Journal of Machine Learning Research 18, 04-2017 2019-07-25
2017 Paolo Toccaceli, Alexander Gammerman
Combination of Conformal Predictors for Classification
published pages: 39-61, ISSN: 1938-7228, DOI:
Proceedings of Machine Learning Research: The Sixth Workshop on Conformal and Probabilistic Prediction and Applications 60, 2017 2019-07-25
2018 Xiangju Qin, Paul Blomstedt, Samuel Kaski
Large-scale probabilistic non-linear matrix factorization for drug discovery
published pages: https://www.ijca, ISSN: , DOI:
3rd International workshop on biomedical informatics with optimization and machine learning 15th April 2019-07-25
2017 Xiangju Qin, Paul Blomstedt, Eemeli Leppäaho, Pekka Parviainen, Samuel Kaski
Distributed Bayesian Matrix Factorization with Limited Communication
published pages: , ISSN: , DOI:
arXiv 02 March 2017 2019-07-25
2017 Günter Klambauer, Thomas Unterthiner, Andreas Mayr, Sepp Hochreiter
Self-Normalizing Neural Networks
published pages: 971--980, ISSN: , DOI:
Advances in Neural Information Processing Systems 30 (NIPS 2017) 4.12-9.12.2017 2019-07-25
2018 Ilia Nouretdinov, Denis Volkhonskiy, Pitt Lim, Paolo Toccaceli, Alexander Gammerman
Inductive Venn-Abers predictive distribution
published pages: 15-36, ISSN: 1938-7228, DOI:
Proceedings of Machine Learning Research: The Seventh Workshop on Conformal and Probabilistic Prediction and Applications 91, 2018 2019-07-25
2017 Günter Klambauer, Thomas Unterthiner, Andreas Mayr, Sepp Hochreiter
Self-Normalizing Neural Networks
published pages: , ISSN: , DOI:
ArXiv 8.6.2017 2019-07-25
2017 Ilia Nouretdinov
Improving Reliable Probabilistic Prediction by Using Additional Knowledge
published pages: 193-200, ISSN: 1938-7228, DOI:
Proceedings of Machine Learning Research: The Sixth Workshop on Conformal and Probabilistic Prediction and Applications 60, 2017 2019-07-25
2017 Tom Vander Aa, Tom Ashby, Yves Vandriessche, Vojtech Cima, Stanislav Böhm, Jan Martinovic
Machine Learning for Chemogenomics on HPC in the ExCAPE Project
published pages: 72 to 74, ISSN: 2308-3484, DOI:
INFOCOMP17 June 25, 2017 2019-07-25
2018 Vladimir Vovk, Claus Bendtsen
Conformal predictive decision making
published pages: 52-62, ISSN: 1938-7228, DOI:
Proceedings of Machine Learning Research: The Seventh Workshop on Conformal and Probabilistic Prediction and Applications 91, 2018 2019-07-25
2017 Denis Volkhonskiy, Evgeny Burnaev, Ilia Nouretdinov, Alexander Gammerman, Vladimir Vovk
Inductive Conformal Martingales for Change-Point Detection
published pages: 132-153, ISSN: 1938-7228, DOI:
Proceedings of Machine Learning Research Proceedings: The Sixth Workshop on Conformal and Probabilistic Prediction and Applications 60, 2017 2019-07-25
2017 Ilia Nouretdinov
Reverse Conformal Approach for On-line Experimental Design
published pages: 185-192, ISSN: 1938-7228, DOI:
Proceedings of Machine Learning Research: The Sixth Workshop on Conformal and Probabilistic Prediction and Applications 60, 2017 2019-07-25
2017 Vladimir Vovk, Jieli Shen, Valery Manokhin, Min-ge Xie
Nonparametric predictive distributions based on conformal prediction
published pages: 82-102, ISSN: 1938-7228, DOI:
Proceedings of Machine Learning Research Proceedings: The Sixth Workshop on Conformal and Probabilistic Prediction and Applications 60, 2017 2019-07-25

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