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2020 – today
- 2025
[e16]Djork-Arné Clevert
, Michael Wand, Kristína Malinovská, Jürgen Schmidhuber, Igor V. Tetko:
AI in Drug Discovery - First International Workshop, AIDD 2024, Held in Conjunction with ICANN 2024, Lugano, Switzerland, September 19, 2024, Proceedings. Lecture Notes in Computer Science 14894, Springer 2025, ISBN 978-3-031-72380-3 [contents]- 2024
- [j68]Peter B. R. Hartog, Fabian Krüger, Samuel Genheden, Igor V. Tetko:
Using test-time augmentation to investigate explainable AI: inconsistencies between method, model and human intuition. J. Cheminformatics 16(1): 39 (2024) - [j67]Varvara Voinarovska, Mikhail Kabeshov, Dmytro Dudenko, Samuel Genheden, Igor V. Tetko:
When Yield Prediction Does Not Yield Prediction: An Overview of the Current Challenges. J. Chem. Inf. Model. 64(1): 42-56 (2024) - [c10]Peter B. R. Hartog, Emma Svensson, Lewis H. Mervin, Samuel Genheden, Ola Engkvist, Igor V. Tetko:
Registries in Machine Learning-Based Drug Discovery: A Shortcut to Code Reuse. AIDD@ICANN 2024: 98-115 - [e15]Michael Wand, Kristína Malinovská, Jürgen Schmidhuber, Igor V. Tetko:
Artificial Neural Networks and Machine Learning - ICANN 2024 - 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024, Proceedings, Part I. Lecture Notes in Computer Science 15016, Springer 2024, ISBN 978-3-031-72331-5 [contents] - [e14]Michael Wand, Kristína Malinovská, Jürgen Schmidhuber, Igor V. Tetko:
Artificial Neural Networks and Machine Learning - ICANN 2024 - 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024, Proceedings, Part II. Lecture Notes in Computer Science 15017, Springer 2024, ISBN 978-3-031-72334-6 [contents] - [e13]Michael Wand, Kristína Malinovská, Jürgen Schmidhuber, Igor V. Tetko:
Artificial Neural Networks and Machine Learning - ICANN 2024 - 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024, Proceedings, Part III. Lecture Notes in Computer Science 15018, Springer 2024, ISBN 978-3-031-72337-7 [contents] - [e12]Michael Wand, Kristína Malinovská, Jürgen Schmidhuber, Igor V. Tetko:
Artificial Neural Networks and Machine Learning - ICANN 2024 - 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024, Proceedings, Part IV. Lecture Notes in Computer Science 15019, Springer 2024, ISBN 978-3-031-72340-7 [contents] - [e11]Michael Wand, Kristína Malinovská, Jürgen Schmidhuber, Igor V. Tetko:
Artificial Neural Networks and Machine Learning - ICANN 2024 - 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024, Proceedings, Part V. Lecture Notes in Computer Science 15020, Springer 2024, ISBN 978-3-031-72343-8 [contents] - [e10]Michael Wand, Kristína Malinovská, Jürgen Schmidhuber, Igor V. Tetko:
Artificial Neural Networks and Machine Learning - ICANN 2024 - 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024, Proceedings, Part VI. Lecture Notes in Computer Science 15021, Springer 2024, ISBN 978-3-031-72346-9 [contents] - [e9]Michael Wand, Kristína Malinovská, Jürgen Schmidhuber, Igor V. Tetko:
Artificial Neural Networks and Machine Learning - ICANN 2024 - 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024, Proceedings, Part VII. Lecture Notes in Computer Science 15022, Springer 2024, ISBN 978-3-031-72349-0 [contents] - [e8]Michael Wand, Kristína Malinovská, Jürgen Schmidhuber, Igor V. Tetko:
Artificial Neural Networks and Machine Learning - ICANN 2024 - 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024, Proceedings, Part VIII. Lecture Notes in Computer Science 15023, Springer 2024, ISBN 978-3-031-72352-0 [contents] - [e7]Michael Wand, Kristína Malinovská, Jürgen Schmidhuber, Igor V. Tetko:
Artificial Neural Networks and Machine Learning - ICANN 2024 - 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024, Proceedings, Part IX. Lecture Notes in Computer Science 15024, Springer 2024, ISBN 978-3-031-72355-1 [contents] - [e6]Michael Wand, Kristína Malinovská, Jürgen Schmidhuber, Igor V. Tetko:
Artificial Neural Networks and Machine Learning - ICANN 2024 - 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024, Proceedings, Part X. Lecture Notes in Computer Science 15025, Springer 2024, ISBN 978-3-031-72358-2 [contents] - [i9]Igor V. Tetko, Ruud van Deursen, Guillaume Godin:
Be aware of overfitting by hyperparameter optimization! CoRR abs/2407.20786 (2024) - 2023
- [i8]Paula Torren-Peraire, Alan Kai Hassen, Samuel Genheden, Jonas Verhoeven, Djork-Arné Clevert, Mike Preuss, Igor V. Tetko:
Models Matter: The Impact of Single-Step Retrosynthesis on Synthesis Planning. CoRR abs/2308.05522 (2023) - 2022
- [j66]Zhonghua Xia, Pavel Karpov, Grzegorz M. Popowicz, Michael Sattler, Igor V. Tetko:
What Features of Ligands Are Relevant to the Opening of Cryptic Pockets in Drug Targets? Informatics 9(1): 8 (2022) - [i7]Alan Kai Hassen, Paula Torren-Peraire, Samuel Genheden, Jonas Verhoeven, Mike Preuss, Igor V. Tetko:
Mind the Retrosynthesis Gap: Bridging the divide between Single-step and Multi-step Retrosynthesis Prediction. CoRR abs/2212.11809 (2022) - 2020
- [j65]Zhonghua Xia, Pavel Karpov, Grzegorz M. Popowicz, Igor V. Tetko:
Focused Library Generator: case of Mdmx inhibitors. J. Comput. Aided Mol. Des. 34(7): 769-782 (2020) - [j64]Pavel Karpov, Guillaume Godin, Igor V. Tetko:
Transformer-CNN: Swiss knife for QSAR modeling and interpretation. J. Cheminformatics 12(1): 17 (2020) - [j63]Ruud van Deursen, Peter Ertl, Igor V. Tetko, Guillaume Godin:
GEN: highly efficient SMILES explorer using autodidactic generative examination networks. J. Cheminformatics 12(1): 22 (2020) - [j62]Ctibor Skuta, Isidro Cortes-Ciriano, Wim Dehaen, Pavel Kríz, Gerard J. P. van Westen, Igor V. Tetko, Andreas Bender, Daniel Svozil:
QSAR-derived affinity fingerprints (part 1): fingerprint construction and modeling performance for similarity searching, bioactivity classification and scaffold hopping. J. Cheminformatics 12(1): 39 (2020) - [j61]Igor V. Tetko, Ola Engkvist:
From Big Data to Artificial Intelligence: chemoinformatics meets new challenges. J. Cheminformatics 12(1): 74 (2020) - [j60]Igor V. Tetko, Alexander Tropsha:
Joint Virtual Special Issue on Computational Toxicology. J. Chem. Inf. Model. 60(3): 1069-1071 (2020) - [i6]Igor V. Tetko, Pavel Karpov, Ruud van Deursen, Guillaume Godin:
Augmented Transformer Achieves 97% and 85% for Top5 Prediction of Direct and Classical Retro-Synthesis. CoRR abs/2003.02804 (2020) - [i5]Ruud van Deursen, Igor V. Tetko, Guillaume Godin:
Beyond Chemical 1D knowledge using Transformers. CoRR abs/2010.01027 (2020)
2010 – 2019
- 2019
- [j59]Sergey Sosnin, Dmitry Karlov, Igor V. Tetko, Maxim V. Fedorov:
Comparative Study of Multitask Toxicity Modeling on a Broad Chemical Space. J. Chem. Inf. Model. 59(3): 1062-1072 (2019) - [c9]Dipan Ghosh, Igor V. Tetko, Bert Klebl, Peter Nussbaumer, Uwe Koch:
Analysis and Modelling of False Positives in GPCR Assays. ICANN (Workshop) 2019: 764-770 - [c8]Pavel Karpov, Guillaume Godin, Igor V. Tetko:
A Transformer Model for Retrosynthesis. ICANN (Workshop) 2019: 817-830 - [c7]Igor V. Tetko, Pavel Karpov, Eric Bruno, Talia B. Kimber, Guillaume Godin:
Augmentation Is What You Need! ICANN (Workshop) 2019: 831-835 - [e5]Igor V. Tetko, Vera Kurková, Pavel Karpov, Fabian J. Theis:
Artificial Neural Networks and Machine Learning - ICANN 2019: Theoretical Neural Computation - 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17-19, 2019, Proceedings, Part I. Lecture Notes in Computer Science 11727, Springer 2019, ISBN 978-3-030-30486-7 [contents] - [e4]Igor V. Tetko, Vera Kurková, Pavel Karpov, Fabian J. Theis:
Artificial Neural Networks and Machine Learning - ICANN 2019: Deep Learning - 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17-19, 2019, Proceedings, Part II. Lecture Notes in Computer Science 11728, Springer 2019, ISBN 978-3-030-30483-6 [contents] - [e3]Igor V. Tetko, Vera Kurková, Pavel Karpov, Fabian J. Theis:
Artificial Neural Networks and Machine Learning - ICANN 2019: Image Processing - 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17-19, 2019, Proceedings, Part III. Lecture Notes in Computer Science 11729, Springer 2019, ISBN 978-3-030-30507-9 [contents] - [e2]Igor V. Tetko, Vera Kurková, Pavel Karpov, Fabian J. Theis:
Artificial Neural Networks and Machine Learning - ICANN 2019: Text and Time Series - 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17-19, 2019, Proceedings, Part IV. Lecture Notes in Computer Science 11730, Springer 2019, ISBN 978-3-030-30489-8 [contents] - [e1]Igor V. Tetko, Vera Kurková, Pavel Karpov, Fabian J. Theis:
Artificial Neural Networks and Machine Learning - ICANN 2019 - 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17-19, 2019, Proceedings - Workshop and Special Sessions. Lecture Notes in Computer Science 11731, Springer 2019, ISBN 978-3-030-30492-8 [contents] - [i4]Ruud van Deursen, Peter Ertl, Igor V. Tetko, Guillaume Godin:
GEN: Highly Efficient SMILES Explorer Using Autodidactic Generative Examination Networks. CoRR abs/1909.04825 (2019) - [i3]Fabio Capela, Vincent Nouchi, Ruud van Deursen, Igor V. Tetko, Guillaume Godin:
Multitask Learning On Graph Neural Networks Applied To Molecular Property Predictions. CoRR abs/1910.13124 (2019) - [i2]Pavel Karpov, Guillaume Godin, Igor V. Tetko:
Transformer-CNN: Fast and Reliable tool for QSAR. CoRR abs/1911.06603 (2019) - 2018
- [j58]Dipan Ghosh, Uwe Koch, Kamyar Hadian, Michael Sattler, Igor V. Tetko:
Luciferase Advisor: High-Accuracy Model To Flag False Positive Hits in Luciferase HTS Assays. J. Chem. Inf. Model. 58(5): 933-942 (2018) - [i1]Talia B. Kimber, Sebastian Engelke, Igor V. Tetko, Eric Bruno, Guillaume Godin:
Synergy Effect between Convolutional Neural Networks and the Multiplicity of SMILES for Improvement of Molecular Prediction. CoRR abs/1812.04439 (2018) - 2016
- [j57]Igor V. Tetko, Daniel M. Lowe, Antony J. Williams:
The development of models to predict melting and pyrolysis point data associated with several hundred thousand compounds mined from PATENTS. J. Cheminformatics 8(1): 2:1-2:18 (2016) - 2014
- [j56]Yurii Sushko, Sergii Novotarskyi, Robert Körner, Joachim Vogt, Ahmed Abdelaziz, Igor V. Tetko:
Prediction-driven matched molecular pairs to interpret QSARs and aid the molecular optimization process. J. Cheminformatics 6(1): 48 (2014) - [j55]Igor V. Tetko, Yurii Sushko, Sergii Novotarskyi, Luc Patiny, Ivan Kondratov, Alexander E. Petrenko, Larisa Charochkina, Abdullah M. Asiri:
How Accurately Can We Predict the Melting Points of Drug-like Compounds? J. Chem. Inf. Model. 54(12): 3320-3329 (2014) - 2013
- [j54]Ioana Oprisiu, Sergii Novotarskyi, Igor V. Tetko:
Modeling of non-additive mixture properties using the Online CHEmical database and Modeling environment (OCHEM). J. Cheminformatics 5: 4 (2013) - [j53]Sergii Novotarskyi, Iurii Sushko, Robert Körner, Igor V. Tetko:
Chemogenomic approach to increase accuracy of QSAR modeling of inhibition activity against five major P450 isoforms. J. Cheminformatics 5(S-1): 23 (2013) - [j52]Ahmed Abdelaziz, Alexander Safanyaev, Vladimir A. Palyulin, Igor V. Tetko:
Building QSAR for HTS in vitro assays - a study for the prediction of Aryl hydrocarbon receptor activators. J. Cheminformatics 5(S-1): 51 (2013) - [j51]Igor V. Tetko, Sergii Novotarskyi, Iurii Sushko, Vladimir Ivanov, Alexander E. Petrenko, Reiner Dieden, Florence Lebon, Benoît Mathieu:
Development of Dimethyl Sulfoxide Solubility Models Using 163 000 Molecules: Using a Domain Applicability Metric to Select More Reliable Predictions. J. Chem. Inf. Model. 53(8): 1990-2000 (2013) - 2012
- [j50]Igor V. Tetko:
The perspectives of computational chemistry modeling. J. Comput. Aided Mol. Des. 26(1): 135-136 (2012) - [j49]Robert Körner, Iurii Sushko, Sergii Novotarskyi, Igor V. Tetko:
In silico pKa prediction. J. Cheminformatics 4(S-1): 55 (2012) - [j48]Ahmed Abdelaziz, Iurii Sushko, Wolfram Teetz, Robert Körner, Sergii Novotarskyi, Igor V. Tetko:
QSAR modeling for In vitro assays: linking ToxCast™ database to the integrated modeling framework "OCHEM". J. Cheminformatics 4(S-1): 62 (2012) - [j47]Stefan Brandmaier, Ullrika Sahlin, Igor V. Tetko, Tomas Öberg:
PLS-Optimal: A Stepwise D-Optimal Design Based on Latent Variables. J. Chem. Inf. Model. 52(4): 975-983 (2012) - [j46]Iurii Sushko, Elena Salmina, Vladimir Potemkin, Gennadiy Poda, Igor V. Tetko:
ToxAlerts: A Web Server of Structural Alerts for Toxic Chemicals and Compounds with Potential Adverse Reactions. J. Chem. Inf. Model. 52(8): 2310-2316 (2012) - 2011
- [j45]Iurii Sushko, Sergii Novotarskyi, Robert Körner, Anil Kumar Pandey, Matthias Rupp, Wolfram Teetz, Stefan Brandmaier, Ahmed Abdelaziz, Volodymyr V. Prokopenko, Vsevolod Yu. Tanchuk, Roberto Todeschini, Alexandre Varnek, Gilles Marcou, Peter Ertl, Vladimir Potemkin, Maria A. Grishina, Johann Gasteiger, Christof H. Schwab, Igor I. Baskin, Vladimir A. Palyulin, Eugene V. Radchenko, William J. Welsh, Vladyslav V. Kholodovych, Dmitriy Chekmarev, Artem Cherkasov, João Aires-de-Sousa, Qing-You Zhang, Andreas Bender, Florian Nigsch, Luc Patiny, Antony J. Williams, Valery Tkachenko, Igor V. Tetko:
Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information. J. Comput. Aided Mol. Des. 25(6): 533-554 (2011) - [j44]Iurii Sushko, Anil Kumar Pandey, Sergii Novotarskyi, Robert Körner, Matthias Rupp, Wolfram Teetz, Stefan Brandmaier, Ahmed Abdelaziz, Volodymyr V. Prokopenko, Vsevolod Yu. Tanchuk, Roberto Todeschini, Alexandre Varnek, Gilles Marcou, Peter Ertl, Vladimir Potemkin, Maria A. Grishina, Johann Gasteiger, Igor I. Baskin, Vladimir A. Palyulin, Eugene V. Radchenko, William J. Welsh, Vladyslav V. Kholodovych, Dmitriy Chekmarev, Artem Cherkasov, João Aires-de-Sousa, Qing-You Zhang, Andreas Bender, Florian Nigsch, Luc Patiny, Antony J. Williams, Valery Tkachenko, Igor V. Tetko:
Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information. J. Cheminformatics 3(S-1): 20 (2011) - [j43]Sergii Novotarskyi, Iurii Sushko, Robert Körner, Anil Kumar Pandey, Igor V. Tetko:
A comparison of different QSAR approaches to modeling CYP450 1A2 inhibition. J. Chem. Inf. Model. 51(6): 1271-1280 (2011) - 2010
- [j42]Sergii Novotarskyi, Iurii Sushko, Robert Körner, Anil Kumar Pandey, Matthias Rupp, Volodymyr V. Prokopenko, Igor V. Tetko:
OCHEM - on-line CHEmical database & modeling environment. J. Cheminformatics 2(S-1): 5 (2010) - [j41]Sergii Novotarskyi, Iurii Sushko, Robert Körner, Anil Kumar Pandey, Igor V. Tetko:
Classification of CYP450 1A2 inhibitors using PubChem data. J. Cheminformatics 2(S-1): 40 (2010) - [j40]Iurii Sushko, Sergii Novotarskyi, Anil Kumar Pandey, Robert Körner, Igor V. Tetko:
Applicability domain for classification problems. J. Cheminformatics 2(S-1): 41 (2010) - [j39]Iurii Sushko, Sergii Novotarskyi, Robert Körner, Anil Kumar Pandey, Artem Cherkasov, Jiazhong Li, Paola Gramatica, Katja Hansen, Timon Schroeter, Klaus-Robert Müller, Lili Xi, Huanxiang Liu, Xiaojun Yao, Tomas Öberg, Farhad Hormozdiari, Phuong Dao, Süleyman Cenk Sahinalp, Roberto Todeschini, Pavel G. Polishchuk, Anatoly G. Artemenko, Victor Kuzmin, Todd Martin, Douglas M. Young, Denis Fourches, Eugene N. Muratov, Alexander Tropsha, Igor I. Baskin, Dragos Horvath, Gilles Marcou, Christophe Muller, Alexandre Varnek, Volodymyr V. Prokopenko, Igor V. Tetko:
Applicability Domains for Classification Problems: Benchmarking of Distance to Models for Ames Mutagenicity Set. J. Chem. Inf. Model. 50(12): 2094-2111 (2010)
2000 – 2009
- 2009
- [j38]Alexandre Varnek, Cédric Gaudin, Gilles Marcou, Igor I. Baskin, Anil Kumar Pandey, Igor V. Tetko:
Inductive Transfer of Knowledge: Application of Multi-Task Learning and Feature Net Approaches to Model Tissue-Air Partition Coefficients. J. Chem. Inf. Model. 49(1): 133-144 (2009) - [p1]Igor V. Tetko:
Associative Neural Network. Artificial Neural Networks 2009: 180-197 - 2008
- [j37]Igor V. Tetko, Igor V. Rodchenkov, Mathias C. Walter, Thomas Rattei, Hans-Werner Mewes:
Beyond the "best" match: machine learning annotation of protein sequences by integration of different sources of information. Bioinform. 24(5): 621-628 (2008) - [j36]Dimitrij Surmeli, Oliver Ratmann, Hans-Werner Mewes, Igor V. Tetko:
FunCat functional inference with belief propagation and feature integration. Comput. Biol. Chem. 32(5): 375-377 (2008) - [j35]Hao Zhu, Alexander Tropsha, Denis Fourches, Alexandre Varnek, Ester Papa, Paola Gramatica, Tomas Öberg, Phuong Dao, Artem Cherkasov, Igor V. Tetko:
Combinatorial QSAR Modeling of Chemical Toxicants Tested against Tetrahymena pyriformis. J. Chem. Inf. Model. 48(4): 766-784 (2008) - [j34]Igor V. Tetko, Iurii Sushko, Anil Kumar Pandey, Hao Zhu, Alexander Tropsha, Ester Papa, Tomas Öberg, Roberto Todeschini, Denis Fourches, Alexandre Varnek:
Critical Assessment of QSAR Models of Environmental Toxicity against Tetrahymena pyriformis: Focusing on Applicability Domain and Overfitting by Variable Selection. J. Chem. Inf. Model. 48(9): 1733-1746 (2008) - 2007
- [j33]Alexandre Varnek, Natalia V. Kireeva, Igor V. Tetko, Igor I. Baskin, Vitaly P. Solov'ev:
Exhaustive QSPR Studies of a Large Diverse Set of Ionic Liquids: How Accurately Can We Predict Melting Points? J. Chem. Inf. Model. 47(3): 1111-1122 (2007) - 2006
- [j32]Igor V. Tetko, Vitaly P. Solov'ev, Alexey V. Antonov, Xiaojun Yao, Jean-Pierre Doucet, Bo Tao Fan, Frank Hoonakker, Denis Fourches, Piere Jost, Nicolas Lachiche, Alexandre Varnek:
Benchmarking of Linear and Nonlinear Approaches for Quantitative Structure-Property Relationship Studies of Metal Complexation with Ionophores. J. Chem. Inf. Model. 46(2): 808-819 (2006) - [j31]Andreas Ruepp, Octave Noubibou Doudieu, Jos van den Oever, Barbara Brauner, Irmtraud Dunger-Kaltenbach, Gisela Fobo, Goar Frishman, Corinna Montrone, Christine Skornia, Steffi Wanka, Thomas Rattei, Philipp Pagel, M. Louise Riley, Dmitrij Frishman, Dimitrij Surmeli, Igor V. Tetko, Matthias Oesterheld, Volker Stümpflen, Hans-Werner Mewes:
The Mouse Functional Genome Database (MfunGD): functional annotation of proteins in the light of their cellular context. Nucleic Acids Res. 34(Database-Issue): 568-571 (2006) - [j30]Igor V. Tetko, Georg Haberer, Stephen Rudd, Blake C. Meyers, Hans-Werner Mewes, Klaus F. X. Mayer:
Spatiotemporal Expression Control Correlates with Intragenic Scaffold Matrix Attachment Regions (S/MARs) in Arabidopsis thaliana. PLoS Comput. Biol. 2(3) (2006) - [j29]Igor V. Tetko, Georg Haberer, Stephen Rudd, Blake C. Meyers, Hans-Werner Mewes, Klaus F. X. Mayer:
Correction: Spatiotemporal Expression Control Correlates with Intragenic Scaffold Matrix Attachment Regions (S/MARs) in Arabidopsis thaliana. PLoS Comput. Biol. 2(6) (2006) - 2005
- [j28]Caroline C. Friedel, Katharina H. V. Jahn, Selina Sommer, Stephen Rudd, Hans-Werner Mewes, Igor V. Tetko:
Support vector machines for separation of mixed plant?Cpathogen EST collections based on codon usage. Bioinform. 21(8): 1383-1388 (2005) - [j27]Igor V. Tetko, Barbara Brauner, Irmtraud Dunger-Kaltenbach, Goar Frishman, Corinna Montrone, Gisela Fobo, Andreas Ruepp, Alexey V. Antonov, Dimitrij Surmeli, Hans-Werner Mewes:
MIPS bacterial genomes functional annotation benchmark dataset. Bioinform. 21(10): 2520-2521 (2005) - [j26]Igor V. Tetko, Axel Facius, Andreas Ruepp, Hans-Werner Mewes:
Super paramagnetic clustering of protein sequences. BMC Bioinform. 6: 82 (2005) - [j25]Yu Wang, Igor V. Tetko, Mark A. Hall, Eibe Frank, Axel Facius, Klaus F. X. Mayer, Hans-Werner Mewes:
Gene selection from microarray data for cancer classification - a machine learning approach. Comput. Biol. Chem. 29(1): 37-46 (2005) - [j24]Alexey V. Antonov, Igor V. Tetko, Denis Kosykh, Dimitrij Surmeli, Hans-Werner Mewes:
Exploiting scale-free information from expression data for cancer classification. Comput. Biol. Chem. 29(4): 288-293 (2005) - [j23]Igor V. Tetko, Johann Gasteiger, Roberto Todeschini, Andrea Mauri, David J. Livingstone, Peter Ertl, Vladimir A. Palyulin, Eugene V. Radchenko, Nikolai S. Zefirov, Alexander S. Makarenko, Vsevolod Yu. Tanchuk, Volodymyr V. Prokopenko:
Virtual Computational Chemistry Laboratory - Design and Description. J. Comput. Aided Mol. Des. 19(6): 453-463 (2005) - [j22]Igor V. Tetko, Ruben Abagyan, Tudor I. Oprea:
Surrogate data - a secure way to share corporate data. J. Comput. Aided Mol. Des. 19(9-10): 749-764 (2005) - [j21]Stephen Rudd, Igor V. Tetko:
Éclair - a web service for unravelling species origin of sequences sampled from mixed host interfaces. Nucleic Acids Res. 33(Web-Server-Issue): 724-727 (2005) - [c6]Alexey V. Antonov, Igor V. Tetko, Denis Kosykh, Dimitrij Surmeli, Hans-Werner Mewes:
Exploiting scale-free information from expression data for cancer classification. German Conference on Bioinformatics 2005: 93-102 - 2004
- [j20]Alexey V. Antonov, Igor V. Tetko, Michael T. Mader, Jan Budczies, Hans-Werner Mewes:
Optimization models for cancer classification: extracting gene interaction information from microarray expression data. Bioinform. 20(5): 644-652 (2004) - [j19]Alexey V. Antonov, Igor V. Tetko, Volodymyr V. Prokopenko, Denis Kosykh, Hans-Werner Mewes:
A web portal for classification of expression data using maximal margin linear programming. Bioinform. 20(17): 3284-3285 (2004) - 2003
- [c5]Alexey V. Antonov, Igor V. Tetko, Michael T. Mader, Jan Budczies, Hans-Werner Mewes:
Exploiting gene interaction information from microarray expression data for cancer classification. German Conference on Bioinformatics 2003: 9-14 - 2002
- [j18]Igor V. Tetko:
Neural Network Studies, 4. Introduction to Associative Neural Networks. J. Chem. Inf. Comput. Sci. 42(3): 717-728 (2002) - [j17]Igor V. Tetko, Vsevolod Yu. Tanchuk:
Application of Associative Neural Networks for Prediction of Lipophilicity in ALOGPS 2.1 Program. J. Chem. Inf. Comput. Sci. 42(5): 1136-1145 (2002) - [j16]Igor V. Tetko:
Associative Neural Network. Neural Process. Lett. 16(2): 187-199 (2002) - 2001
- [j15]Alessandro E. P. Villa, Igor V. Tetko, Javier Iglesias:
Computer assisted neurophysiological analysis of cell assemblies activity. Neurocomputing 38-40: 1025-1030 (2001) - [j14]Igor V. Tetko, Alessandro E. P. Villa:
Pattern grouping algorithm and de-convolution filtering of non-stationary correlated Poisson processes. Neurocomputing 38-40: 1709-1714 (2001) - [j13]Igor V. Tetko, Vsevolod Yu. Tanchuk, Tamara N. Kasheva, Alessandro E. P. Villa:
Internet Software for the Calculation of the Lipophilicity and Aqueous Solubility of Chemical Compounds. J. Chem. Inf. Comput. Sci. 41(2): 246-252 (2001) - [j12]Igor V. Tetko, Vsevolod Yu. Tanchuk, Alessandro E. P. Villa:
Prediction of n-Octanol/Water Partition Coefficients from PHYSPROP Database Using Artificial Neural Networks and E-State Indices. J. Chem. Inf. Comput. Sci. 41(5): 1407-1421 (2001) - [j11]Igor V. Tetko, Vsevolod Yu. Tanchuk, Tamara N. Kasheva, Alessandro E. P. Villa:
Estimation of Aqueous Solubility of Chemical Compounds Using E-State Indices. J. Chem. Inf. Comput. Sci. 41(6): 1488-1493 (2001) - 2000
- [j10]Jarmo J. Huuskonen, David J. Livingstone, Igor V. Tetko:
Neural Network Modeling for Estimation of Partition Coefficient Based on Atom-Type Electrotopological State Indices. J. Chem. Inf. Comput. Sci. 40(4): 947-955 (2000)
1990 – 1999
- 1998
- [j9]Vasyl V. Kovalishyn, Igor V. Tetko, Alexander I. Luik, Vladyslav V. Kholodovych, Alessandro E. P. Villa, David J. Livingstone:
Neural Network Studies. 3. Variable Selection in the Cascade-Correlation Learning Architecture. J. Chem. Inf. Comput. Sci. 38(4): 651-659 (1998) - [j8]Igor V. Tetko, Alessandro E. P. Villa, Tatyana I. Aksenova, Walter L. Zielinski, James Brower, Elizabeth R. Collantes, William J. Welsh:
Application of a Pruning Algorithm To Optimize Artificial Neural Networks for Pharmaceutical Fingerprinting. J. Chem. Inf. Comput. Sci. 38(4): 660-668 (1998) - [c4]Luc Jeandenans, Michel Gautero, François Grize, Igor V. Tetko, Alessandro E. P. Villa:
Computer assisted neurophysiology by a distributed JAVA program. HCC 1998: 261-272 - 1997
- [j7]Igor V. Tetko, Alessandro E. P. Villa:
Fast combinatorial methods to estimate the probability of complex temporal patterns of spikes. Biol. Cybern. 76(5): 397-408 (1997) - [j6]David J. Livingstone, David T. Manallack, Igor V. Tetko:
Data modelling with neural networks: Advantages and limitations. J. Comput. Aided Mol. Des. 11(2): 135-142 (1997) - [j5]Igor V. Tetko, Alessandro E. P. Villa:
Efficient Partition of Learning Data Sets for Neural Network Training. Neural Networks 10(8): 1361-1374 (1997) - [j4]Igor V. Tetko, Alessandro E. P. Villa:
An Enhancement of Generalization Ability in Cascade Correlation Algorithm by Avoidance of Overfitting/Overtraining Problem. Neural Process. Lett. 6(1-2): 43-50 (1997) - [j3]Igor V. Tetko, Alessandro E. P. Villa:
An Efficient Partition of Training Data Set Improves Speed and Accuracy of Cascade-correlation Algorithm. Neural Process. Lett. 6(1-2): 51-59 (1997) - [c3]Igor V. Tetko, Alessandro E. P. Villa:
A Comparative Study of Pattern Detection Algorithm and Dynamical System Approach Using Simulated Spike Trains. ICANN 1997: 37-42 - 1996
- [j2]Igor V. Tetko, Alessandro E. P. Villa, David J. Livingstone:
Neural Network Studies, 2. Variable Selection. J. Chem. Inf. Comput. Sci. 36(4): 794-803 (1996) - 1995
- [j1]Igor V. Tetko, David J. Livingstone, Alexander I. Luik:
Neural network studies, 1. Comparison of overfitting and overtraining. J. Chem. Inf. Comput. Sci. 35(5): 826-833 (1995) - 1994
- [c2]Igor V. Tetko, Vsevolod Yu. Tanchuk, Alexander I. Luik:
Evaluation of potential HIV-1 reverse transcriptase inhibitors by artificial neural networks. CBMS 1994: 311-316 - [c1]Igor V. Tetko, Alexander I. Luik:
Input Parameters' estimation via neural networks. ESANN 1994
Coauthor Index
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