Exascale Compound Activity Prediction Engine

Project Information

ExCAPE

Grant agreement ID: 671555

DOI 10.3030/671555

Project closed

EC signature date 24 June 2015

Start date 1 September 2015

End date 31 August 2018

Funded under

• EXCELLENT SCIENCE - Future and Emerging Technologies (FET)

Total cost € 3 910 140,00

EU contribution € 3 910 140,00

Coordinated by

INTERUNIVERSITAIR MICRO-ELECTRONICA CENTRUM
Belgium

Deliverables

Demonstrators, pilots, prototypes (1)

Workshop 2

Workshop for PRACE and/or QSAR community or similat

Documents, reports (13)

Factsheet 2

Factsheet for dissemination

Development report

Development report: Document describing the impact of the algorithmic developments on the industry challenges

Simulation report 3

Report: Node peformance simulation of D2.11

Simulation report 2

Report: Node performance simulation of D2.7

Simulation Report 1

Report: Node performance simulation of D2.3

Criteria report

Benchmarking criteria

Metamodel report

Document describing industry challenges and academic research initiatives, and overlap between different ML algorithms to guide building the meta-model

Final simulation and scalability report (4 and 2)

Report: Node performance simulation of D2.17, and scalability test

Factsheet 1

Factsheet for exploitation

Scalability report 1

Scalability test

Challenge report

Document describing challenges, data types and pipelines used in building Chemogenomics models

Report + Code 6

Code for Platt scaling and its integration with algorithms from earlier deliverables corresponding to Task 1.2.3.

Workflows report

Overview of developed methods and workflows

Other (5)

PublicCancer

Public cancer cell line datasets

Report + Code 4

Code for Exascale Bayesian Non-linear Multi-view Matrix Factorization and related documentation, deep learning code and related documentation version 3. Code for integration of unsupervised pre-processing and supervised learning, and related documentation. Code for Venn-Abers probabilistic predictors for large and imbalanced datasets

WebData

Web APIs for public datasets

Tox

Toxicology datasets

PublicBio

Public biological datasets

Websites, patent fillings, videos etc. (2)

Social

Social media material

Website

Project website online

Publications

Conference proceedings (18)

Combination of Conformal Predictors for Classification

Author(s): Paolo Toccaceli, Alexander Gammerman
Published in: Proceedings of Machine Learning Research: The Sixth Workshop on Conformal and Probabilistic Prediction and Applications, Issue 60, 2017, 2017, Page(s) 39-61, ISSN 1938-7228
Publisher: JMLR Inc. and Microtome Publishing (United States)

Nonparametric predictive distributions based on conformal prediction

Author(s): Vladimir Vovk, Jieli Shen, Valery Manokhin, Min-ge Xie
Published in: Proceedings of Machine Learning Research Proceedings: The Sixth Workshop on Conformal and Probabilistic Prediction and Applications, Issue 60, 2017, 2017, Page(s) 82-102, ISSN 1938-7228
Publisher: JMLR Inc. and Microtome Publishing (United States)

Reverse Conformal Approach for On-line Experimental Design

Author(s): Ilia Nouretdinov
Published in: Proceedings of Machine Learning Research: The Sixth Workshop on Conformal and Probabilistic Prediction and Applications, Issue 60, 2017, 2017, Page(s) 185-192, ISSN 1938-7228
Publisher: JMLR Inc. and Microtome Publishing (United States)

Improving Reliable Probabilistic Prediction by Using Additional Knowledge

Author(s): Ilia Nouretdinov
Published in: Proceedings of Machine Learning Research: The Sixth Workshop on Conformal and Probabilistic Prediction and Applications, Issue 60, 2017, 2017, Page(s) 193-200, ISSN 1938-7228
Publisher: JMLR Inc. and Microtome Publishing (United States)

Machine Learning for Chemogenomics on HPC in the ExCAPE Project

Author(s): Tom Vander Aa, Tom Ashby, Yves Vandriessche, Vojtech Cima, Stanislav Böhm, Jan Martinovic
Published in: INFOCOMP17, Issue June 25, 2017, 2017, Page(s) 72 to 74, ISSN 2308-3484
Publisher: IARIA

Inductive Conformal Martingales for Change-Point Detection

Author(s): Denis Volkhonskiy, Evgeny Burnaev, Ilia Nouretdinov, Alexander Gammerman, Vladimir Vovk
Published in: Proceedings of Machine Learning Research Proceedings: The Sixth Workshop on Conformal and Probabilistic Prediction and Applications, Issue 60, 2017, 2017, Page(s) 132-153, ISSN 1938-7228
Publisher: JMLR Inc. and Microtome Publishing (United States)

Inductive Venn-Abers predictive distribution

Author(s): Ilia Nouretdinov, Denis Volkhonskiy, Pitt Lim, Paolo Toccaceli, Alexander Gammerman
Published in: Proceedings of Machine Learning Research: The Seventh Workshop on Conformal and Probabilistic Prediction and Applications, Issue 91, 2018, 2018, Page(s) 15-36, ISSN 1938-7228
Publisher: JMLR Inc. and Microtome Publishing (United States)

Conformal predictive decision making

Author(s): Vladimir Vovk, Claus Bendtsen
Published in: Proceedings of Machine Learning Research: The Seventh Workshop on Conformal and Probabilistic Prediction and Applications, Issue 91, 2018, 2018, Page(s) 52-62, ISSN 1938-7228
Publisher: JMLR Inc. and Microtome Publishing (United States)

Large-scale probabilistic non-linear matrix factorization for drug discovery

Author(s): Xiangju Qin, Paul Blomstedt, Samuel Kaski
Published in: 3rd International workshop on biomedical informatics with optimization and machine learning, Issue 15th April, 2018, Page(s) https://www.ijcai-boom.org/uploads/5/1/6/8/51680821/xiangju_qu.pdf
Publisher: https://www.ijcai-boom.org/proceeding.html

Self-Normalizing Neural Networks

Author(s): Günter Klambauer, Thomas Unterthiner, Andreas Mayr, Sepp Hochreiter
Published in: Advances in Neural Information Processing Systems 30 (NIPS 2017), Issue 4.12-9.12.2017, 2017, Page(s) 971--980
Publisher: Curran Associates, Inc.

HyperLoom - A Platform for Defining and Executing Scientific Pipelines in Distributed Environments

Author(s): Vojtěch Cima, Stanislav Böhm, Jan Martinovič, Jiří Dvorský, Kateřina Janurová, Tom Vander Aa, Thomas J. Ashby, Vladimir Chupakhin
Published in: Proceedings of the 9th Workshop and 7th Workshop on Parallel Programming and RunTime Management Techniques for Manycore Architectures and Design Tools and Architectures for Multicore Embedded Computing Platforms - PARMA-DITAM '18, 2018, Page(s) 1-6, ISBN 9781-450364447
Publisher: ACM Press
DOI: 10.1145/3183767.3183768

Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)

Author(s): Djork-Arne Clevert, Thomas Unterthiner, Sepp Hochreiter
Published in: CoRR, Issue abs/1511.07289, 2015, Page(s) 1-14
Publisher: International Conference on Learning Representations (ICLR) 2016

Speeding up Semantic Segmentation for Autonomous Driving

Author(s): 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
Published in: OpenReview, 2016, Page(s) 1-7
Publisher: Workshop on Machine Learning for Intelligent Transport Systems, Conference Neural Information Processing Systems Foundation (NIPS 2016)

Criteria of Efficiency for Conformal Prediction

Author(s): Vladimir Vovk, Valentina Fedorova, Ilia Nouretdinov, Alexander Gammerman
Published in: COPA 2016: Conformal and Probabilistic Prediction with Applications, 2016, Page(s) 23-39
Publisher: Springer International Publishing
DOI: 10.1007/978-3-319-33395-3_2

Distributed Bayesian Probabilistic Matrix Factorization

Author(s): Tom Vander Aa, Imen Chakroun, Tom Haber
Published in: 2016 IEEE International Conference on Cluster Computing (CLUSTER), 2016, Page(s) 346-349, ISBN 978-1-5090-3653-0
Publisher: IEEE
DOI: 10.1109/CLUSTER.2016.13

Universal Probability-Free Conformal Prediction

Author(s): Vladimir Vovk, Dusko Pavlovic
Published in: COPA 2016: Conformal and Probabilistic Prediction with Applications, 2016, Page(s) 40-47
Publisher: Springer International Publishing
DOI: 10.1007/978-3-319-33395-3_3

Distributed Conformal Anomaly Detection

Author(s): Ilia Nouretdinov
Published in: 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), 2016, Page(s) 253-258, ISBN 978-1-5090-6167-9
Publisher: IEEE
DOI: 10.1109/ICMLA.2016.0049

Conformal Predictors for Compound Activity Prediction

Author(s): Paolo Toccaceli, Ilia Nouretdinov, Alexander Gammerman
Published in: COPA 2016: Conformal and Probabilistic Prediction with Applications, 2016, Page(s) 51-66
Publisher: Springer International Publishing
DOI: 10.1007/978-3-319-33395-3_4

Other (3)

Self-Normalizing Neural Networks

Author(s): Günter Klambauer, Thomas Unterthiner, Andreas Mayr, Sepp Hochreiter
Published in: ArXiv, Issue 8.6.2017, 2017
Publisher: arXiv preprint arXiv:1706.02515

Distributed Bayesian Matrix Factorization with Limited Communication

Author(s): Xiangju Qin, Paul Blomstedt, Eemeli Leppäaho, Pekka Parviainen, Samuel Kaski
Published in: arXiv, Issue 02 March 2017, 2017
Publisher: Cornell University Library

SMURFF: a High-Performance Framework for Matrix Factorization

Author(s): Tom Vander Aa and Tom Ashby
Published in: n/a, Issue n/a, 2018
Publisher: EPCC

Peer reviewed articles (18)

Exploratory Analysis of Multiple Data Sources with Group Factor

Author(s): Eemeli Leppäaho, Muhammad Ammad-ud-din, Samuel Kaski
Published in: Journal of Machine Learning Research, Issue 18, 04-2017, 2017, Page(s) 1-5, ISSN 1533-7928
Publisher: JMLR Inc. and Microtome Publishing (United States)

Criteria of efficiency for set-valued classification

Author(s): Vladimir Vovk, Ilia Nouretdinov, Valentina Fedorova, Ivan Petej, Alex Gammerman
Published in: Annals of Mathematics and Artificial Intelligence, Issue 81/1-2, 2017, Page(s) 21-46, ISSN 1012-2443
Publisher: Kluwer Academic Publishers
DOI: 10.1007/s10472-017-9540-3

The role of measurability in game-theoretic probability

Author(s): Vladimir Vovk
Published in: Finance and Stochastics, Issue 21/3, 2017, Page(s) 719-739, ISSN 0949-2984
Publisher: Springer Verlag
DOI: 10.1007/s00780-017-0336-4

panelcn.MOPS: Copy-number detection in targeted NGS panel data for clinical diagnostics

Author(s): Gundula Povysil, Antigoni Tzika, Julia Vogt, Verena Haunschmid, Ludwine Messiaen, Johannes Zschocke, Günter Klambauer, Sepp Hochreiter, Katharina Wimmer
Published in: Human Mutation, Issue 38/7, 2017, Page(s) 889-897, ISSN 1059-7794
Publisher: John Wiley & Sons Inc.
DOI: 10.1002/humu.23237

Universal probability-free prediction

Author(s): Vladimir Vovk, Dusko Pavlovic
Published in: Annals of Mathematics and Artificial Intelligence, Issue 81/1-2, 2017, Page(s) 47-70, ISSN 1012-2443
Publisher: Kluwer Academic Publishers
DOI: 10.1007/s10472-017-9547-9

Ambit-SMIRKS: a software module for reaction representation, reaction search and structure transformation

Author(s): Nikolay Kochev, Svetlana Avramova, Nina Jeliazkova
Published in: Journal of Cheminformatics, Issue 10/1, 2018, ISSN 1758-2946
Publisher: Chemistry Central
DOI: 10.1186/s13321-018-0295-6

Conformal prediction of biological activity of chemical compounds

Author(s): Paolo Toccaceli, Ilia Nouretdinov, Alexander Gammerman
Published in: Annals of Mathematics and Artificial Intelligence, Issue 81/1-2, 2017, Page(s) 105-123, ISSN 1012-2443
Publisher: Kluwer Academic Publishers
DOI: 10.1007/s10472-017-9556-8

Combination of inductive mondrian conformal predictors

Author(s): Paolo Toccaceli, Alexander Gammerman
Published in: Machine Learning, 2018, ISSN 0885-6125
Publisher: Kluwer Academic Publishers
DOI: 10.1007/s10994-018-5754-9

Nonparametric predictive distributions based on conformal prediction

Author(s): Vladimir Vovk, Jieli Shen, Valery Manokhin, Min-ge Xie
Published in: Machine Learning, 2018, ISSN 0885-6125
Publisher: Kluwer Academic Publishers
DOI: 10.1007/s10994-018-5755-8

Towards a Scalable Software Defined Network-on-Chip for Next Generation Cloud

Author(s): Alberto Scionti, Somnath Mazumdar, Antoni Portero
Published in: Sensors, Issue 18/7, 2018, Page(s) 2330, ISSN 1424-8220
Publisher: Multidisciplinary Digital Publishing Institute (MDPI)
DOI: 10.3390/s18072330

Large-scale comparison of machine learning methods for drug target prediction on ChEMBL

Author(s): Andreas Mayr, Günter Klambauer, Thomas Unterthiner, Marvin Steijaert, Jörg K. Wegner, Hugo Ceulemans, Djork-Arné Clevert, Sepp Hochreiter
Published in: Chemical Science, Issue 9/24, 2018, Page(s) 5441-5451, ISSN 2041-6520
Publisher: Royal Society of Chemistry
DOI: 10.1039/c8sc00148k

Validity and efficiency of conformal anomaly detection on big distributed data

Author(s): Ilia Nouretdinov
Published in: Advances in Science, Technology and Engineering Systems Journal, Issue 2/3, 2017, Page(s) 254-267, ISSN 2415-6698
Publisher: ASTES Publishers
DOI: 10.25046/aj020335

Purely pathwise probability-free Ito integral

Author(s): V. Vovk
Published in: Matematychni Studii, Issue 46/1, 2017, ISSN 1027-4634
Publisher: the Lviv Mathematical Society
DOI: 10.15330/ms.46.1.96-110

Distributed Bayesian Probabilistic Matrix Factorization

Author(s): Tom Vander Aa, Imen Chakroun, Tom Haber
Published in: Procedia Computer Science, Issue 108, 2017, Page(s) 1030-1039, ISSN 1877-0509
Publisher: Elsevier
DOI: 10.1016/j.procs.2017.05.009

SW-SGD: The Sliding Window Stochastic Gradient Descent Algorithm

Author(s): Imen Chakroun, Tom Haber, Thomas J. Ashby
Published in: Procedia Computer Science, Issue 108, 2017, Page(s) 2318-2322, ISSN 1877-0509
Publisher: Elsevier
DOI: 10.1016/j.procs.2017.05.082

Improving Operational Intensity in Data Bound Markov Chain Monte Carlo

Author(s): Balazs Nemeth, Tom Haber, Thomas J. Ashby, Wim Lamotte
Published in: Procedia Computer Science, Issue 108, 2017, Page(s) 2348-2352, ISSN 1877-0509
Publisher: Elsevier
DOI: 10.1016/j.procs.2017.05.024

Hypergraphical Conformal Predictors

Author(s): Valentina Fedorova, Alex Gammerman, Ilia Nouretdinov, Vladimir Vovk
Published in: International Journal on Artificial Intelligence Tools, Issue 24/06, 2015, Page(s) 1560003, ISSN 0218-2130
Publisher: World Scientific Publishing Co
DOI: 10.1142/S0218213015600039

ExCAPE-DB: an integrated large scale dataset facilitating Big Data analysis in chemogenomics

Author(s): 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
Published in: Journal of Cheminformatics, Issue 9/1, 2017, ISSN 1758-2946
Publisher: Chemistry Central
DOI: 10.1186/s13321-017-0203-5

Book chapters (1)

HyperLoom Possibilities for Executing Scientific Workflows on the Cloud

Author(s): Vojtech Cima, Stanislav Böhm, Jan Martinovič, Jiří Dvorský, Thomas J. Ashby, Vladimir Chupakhin
Published in: Complex, Intelligent, and Software Intensive Systems, Issue 611, 2018, Page(s) 397-406, ISBN 978-3-319-61565-3
Publisher: Springer International Publishing
DOI: 10.1007/978-3-319-61566-0_36

Non-peer reviewed articles (1)

Application of Bioactivity Profile Based Fingerprints for Building Machine Learning Models

Author(s): Noé Sturm, Jiangming Sun, Yves Vandriessche, Andreas Mayr, Günter Klambauer, Lars-Anders Carlson, Ola Engkvist, Hongming Chen
Published in: ChemRxiv, 2018, ISSN 2573-2293
Publisher: American Chemical Society
DOI: 10.26434/chemrxiv.6969584.v1