Exascale Compound Activity Prediction Engine

Livrables

Workshop 2

Workshop for PRACE and/or QSAR community or similat

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

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

Social

Social media material

Website

Project website online

Publications

Combination of Conformal Predictors for Classification

Auteurs: Paolo Toccaceli, Alexander Gammerman
Publié dans: Proceedings of Machine Learning Research: The Sixth Workshop on Conformal and Probabilistic Prediction and Applications, Numéro 60, 2017, 2017, Page(s) 39-61, ISSN 1938-7228
Éditeur: JMLR Inc. and Microtome Publishing (United States)

Nonparametric predictive distributions based on conformal prediction

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

Reverse Conformal Approach for On-line Experimental Design

Auteurs: Ilia Nouretdinov
Publié dans: Proceedings of Machine Learning Research: The Sixth Workshop on Conformal and Probabilistic Prediction and Applications, Numéro 60, 2017, 2017, Page(s) 185-192, ISSN 1938-7228
Éditeur: JMLR Inc. and Microtome Publishing (United States)

Improving Reliable Probabilistic Prediction by Using Additional Knowledge

Auteurs: Ilia Nouretdinov
Publié dans: Proceedings of Machine Learning Research: The Sixth Workshop on Conformal and Probabilistic Prediction and Applications, Numéro 60, 2017, 2017, Page(s) 193-200, ISSN 1938-7228
Éditeur: JMLR Inc. and Microtome Publishing (United States)

Machine Learning for Chemogenomics on HPC in the ExCAPE Project

Auteurs: Tom Vander Aa, Tom Ashby, Yves Vandriessche, Vojtech Cima, Stanislav Böhm, Jan Martinovic
Publié dans: INFOCOMP17, Numéro June 25, 2017, 2017, Page(s) 72 to 74, ISSN 2308-3484
Éditeur: IARIA

Inductive Conformal Martingales for Change-Point Detection

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

Inductive Venn-Abers predictive distribution

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

Conformal predictive decision making

Auteurs: Vladimir Vovk, Claus Bendtsen
Publié dans: Proceedings of Machine Learning Research: The Seventh Workshop on Conformal and Probabilistic Prediction and Applications, Numéro 91, 2018, 2018, Page(s) 52-62, ISSN 1938-7228
Éditeur: JMLR Inc. and Microtome Publishing (United States)

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

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

Self-Normalizing Neural Networks

Auteurs: Günter Klambauer, Thomas Unterthiner, Andreas Mayr, Sepp Hochreiter
Publié dans: Advances in Neural Information Processing Systems 30 (NIPS 2017), Numéro 4.12-9.12.2017, 2017, Page(s) 971--980
Éditeur: Curran Associates, Inc.

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

Auteurs: Vojtěch Cima, Stanislav Böhm, Jan Martinovič, Jiří Dvorský, Kateřina Janurová, Tom Vander Aa, Thomas J. Ashby, Vladimir Chupakhin
Publié dans: 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
Éditeur: ACM Press
DOI: 10.1145/3183767.3183768

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

Auteurs: Djork-Arne Clevert, Thomas Unterthiner, Sepp Hochreiter
Publié dans: CoRR, Numéro abs/1511.07289, 2015, Page(s) 1-14
Éditeur: International Conference on Learning Representations (ICLR) 2016

Speeding up Semantic Segmentation for Autonomous Driving

Auteurs: 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
Publié dans: OpenReview, 2016, Page(s) 1-7
Éditeur: Workshop on Machine Learning for Intelligent Transport Systems, Conference Neural Information Processing Systems Foundation (NIPS 2016)

Criteria of Efficiency for Conformal Prediction

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

Distributed Bayesian Probabilistic Matrix Factorization

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

Universal Probability-Free Conformal Prediction

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

Distributed Conformal Anomaly Detection

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

Conformal Predictors for Compound Activity Prediction

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

Self-Normalizing Neural Networks

Auteurs: Günter Klambauer, Thomas Unterthiner, Andreas Mayr, Sepp Hochreiter
Publié dans: ArXiv, Numéro 8.6.2017, 2017
Éditeur: arXiv preprint arXiv:1706.02515

Distributed Bayesian Matrix Factorization with Limited Communication

Auteurs: Xiangju Qin, Paul Blomstedt, Eemeli Leppäaho, Pekka Parviainen, Samuel Kaski
Publié dans: arXiv, Numéro 02 March 2017, 2017
Éditeur: Cornell University Library

SMURFF: a High-Performance Framework for Matrix Factorization

Auteurs: Tom Vander Aa and Tom Ashby
Publié dans: n/a, Numéro n/a, 2018
Éditeur: EPCC

Exploratory Analysis of Multiple Data Sources with Group Factor

Auteurs: Eemeli Leppäaho, Muhammad Ammad-ud-din, Samuel Kaski
Publié dans: Journal of Machine Learning Research, Numéro 18, 04-2017, 2017, Page(s) 1-5, ISSN 1533-7928
Éditeur: JMLR Inc. and Microtome Publishing (United States)

Criteria of efficiency for set-valued classification

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

The role of measurability in game-theoretic probability

Auteurs: Vladimir Vovk
Publié dans: Finance and Stochastics, Numéro 21/3, 2017, Page(s) 719-739, ISSN 0949-2984
Éditeur: Springer Verlag
DOI: 10.1007/s00780-017-0336-4

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

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

Universal probability-free prediction

Auteurs: Vladimir Vovk, Dusko Pavlovic
Publié dans: Annals of Mathematics and Artificial Intelligence, Numéro 81/1-2, 2017, Page(s) 47-70, ISSN 1012-2443
Éditeur: Kluwer Academic Publishers
DOI: 10.1007/s10472-017-9547-9

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

Auteurs: Nikolay Kochev, Svetlana Avramova, Nina Jeliazkova
Publié dans: Journal of Cheminformatics, Numéro 10/1, 2018, ISSN 1758-2946
Éditeur: Chemistry Central
DOI: 10.1186/s13321-018-0295-6

Conformal prediction of biological activity of chemical compounds

Auteurs: Paolo Toccaceli, Ilia Nouretdinov, Alexander Gammerman
Publié dans: Annals of Mathematics and Artificial Intelligence, Numéro 81/1-2, 2017, Page(s) 105-123, ISSN 1012-2443
Éditeur: Kluwer Academic Publishers
DOI: 10.1007/s10472-017-9556-8

Combination of inductive mondrian conformal predictors

Auteurs: Paolo Toccaceli, Alexander Gammerman
Publié dans: Machine Learning, 2018, ISSN 0885-6125
Éditeur: Kluwer Academic Publishers
DOI: 10.1007/s10994-018-5754-9

Nonparametric predictive distributions based on conformal prediction

Auteurs: Vladimir Vovk, Jieli Shen, Valery Manokhin, Min-ge Xie
Publié dans: Machine Learning, 2018, ISSN 0885-6125
Éditeur: Kluwer Academic Publishers
DOI: 10.1007/s10994-018-5755-8

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

Auteurs: Alberto Scionti, Somnath Mazumdar, Antoni Portero
Publié dans: Sensors, Numéro 18/7, 2018, Page(s) 2330, ISSN 1424-8220
Éditeur: Multidisciplinary Digital Publishing Institute (MDPI)
DOI: 10.3390/s18072330

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

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

Validity and efficiency of conformal anomaly detection on big distributed data

Auteurs: Ilia Nouretdinov
Publié dans: Advances in Science, Technology and Engineering Systems Journal, Numéro 2/3, 2017, Page(s) 254-267, ISSN 2415-6698
Éditeur: ASTES Publishers
DOI: 10.25046/aj020335

Purely pathwise probability-free Ito integral

Auteurs: V. Vovk
Publié dans: Matematychni Studii, Numéro 46/1, 2017, ISSN 1027-4634
Éditeur: the Lviv Mathematical Society
DOI: 10.15330/ms.46.1.96-110

Distributed Bayesian Probabilistic Matrix Factorization

Auteurs: Tom Vander Aa, Imen Chakroun, Tom Haber
Publié dans: Procedia Computer Science, Numéro 108, 2017, Page(s) 1030-1039, ISSN 1877-0509
Éditeur: Elsevier
DOI: 10.1016/j.procs.2017.05.009

SW-SGD: The Sliding Window Stochastic Gradient Descent Algorithm

Auteurs: Imen Chakroun, Tom Haber, Thomas J. Ashby
Publié dans: Procedia Computer Science, Numéro 108, 2017, Page(s) 2318-2322, ISSN 1877-0509
Éditeur: Elsevier
DOI: 10.1016/j.procs.2017.05.082

Improving Operational Intensity in Data Bound Markov Chain Monte Carlo

Auteurs: Balazs Nemeth, Tom Haber, Thomas J. Ashby, Wim Lamotte
Publié dans: Procedia Computer Science, Numéro 108, 2017, Page(s) 2348-2352, ISSN 1877-0509
Éditeur: Elsevier
DOI: 10.1016/j.procs.2017.05.024

Hypergraphical Conformal Predictors

Auteurs: Valentina Fedorova, Alex Gammerman, Ilia Nouretdinov, Vladimir Vovk
Publié dans: International Journal on Artificial Intelligence Tools, Numéro 24/06, 2015, Page(s) 1560003, ISSN 0218-2130
Éditeur: World Scientific Publishing Co
DOI: 10.1142/S0218213015600039

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

Auteurs: 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
Publié dans: Journal of Cheminformatics, Numéro 9/1, 2017, ISSN 1758-2946
Éditeur: Chemistry Central
DOI: 10.1186/s13321-017-0203-5

HyperLoom Possibilities for Executing Scientific Workflows on the Cloud

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

Application of Bioactivity Profile Based Fingerprints for Building Machine Learning Models

Auteurs: Noé Sturm, Jiangming Sun, Yves Vandriessche, Andreas Mayr, Günter Klambauer, Lars-Anders Carlson, Ola Engkvist, Hongming Chen
Publié dans: ChemRxiv, 2018, ISSN 2573-2293
Éditeur: American Chemical Society
DOI: 10.26434/chemrxiv.6969584.v1

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