F. Železný

发表

Estimating sequence similarity from read sets for clustering next-generation sequencing data

Petr Rysavý, Filip Zelezný, F. Zelezný, 2018, Data Mining and Knowledge Discovery.

Estimating Sequence Similarity from Read Sets for Clustering Sequencing Data

Petr Rysavý, Filip Zelezný, F. Zelezný, 2016, IDA.

Comparative evaluation of set-level techniques in predictive classification of gene expression samples

Filip Zelezný, Jirí Kléma, Jakub Tolar, 2012, BMC Bioinformatics.

Gaussian Logic for Predictive Classification

Ondrej Kuzelka, Filip Zelezný, Matej Holec, 2011, ECML/PKDD.

Beyond graph neural networks with lifted relational neural networks pdf

Gustav Sourek, Filip Zelezny, Ondrej Kuzelka, 2020, Machine Learning.

Tubular Atrophy and Low Netrin-1 Gene Expression Are Associated With Delayed Kidney Allograft Function

F. Zelezný, O. Viklicky, P. Baláž, 2014, Transplantation.

Exploiting sports-betting market using machine learning

Ondřej Hubáček, Gustav Sourek, Filip Železný, 2019, International Journal of Forecasting.

Learning to predict soccer results from relational data with gradient boosted trees

Ondřej Hubáček, Filip Železný, Gustav Šourek, 2018, Machine Learning.

Deep Learning from Spatial Relations for Soccer Pass Prediction

Filip Zelezný, Gustav Sourek, Ondrej Hubácek, 2018, MLSA@PKDD/ECML.

Lifted Relational Neural Networks: Efficient Learning of Latent Relational Structures

Steven Schockaert, Ondrej Kuzelka, Filip Zelezný, 2018, J. Artif. Intell. Res..

Stacked Structure Learning for Lifted Relational Neural Networks

Steven Schockaert, Ondrej Kuzelka, Filip Zelezný, 2017, ILP.

Score-based soccer match outcome modeling – an experimental review

F. Zelezný, Gustav Sourek, O. Hubácek, 2019 .

Learning to predict soccer results from relational data with gradient boosted trees

Gustav Sourek, O. Hubáček, F. Železný, 2018, Machine Learning.

Formulating the template ILP consistency problem as a constraint satisfaction problem

Roman Barták, Ondrej Kuzelka, Filip Zelezný, 2013, Constraints.

Using Constraint Satisfaction for Learning Hypotheses in Inductive Logic Programming

Roman Barták, Ondrej Kuzelka, Filip Zelezný, 2010, FLAIRS.

Formulating Template Consistency in Inductive Logic Programming as a Constraint Satisfaction Problem

Roman Barták, Ondrej Kuzelka, Filip Zelezný, 2010, Abstraction, Reformulation, and Approximation.

Lattice-Search Runtime Distributions May Be Heavy-Tailed

Ashwin Srinivasan, David Page, Filip Zelezný, 2002, ILP.

RSD: Relational Subgroup Discovery through First-Order Feature Construction

Peter A. Flach, Nada Lavrac, Filip Zelezný, 2002, ILP.

Comparative Evaluation of Set-Level Techniques in Microarray Classification

Filip Zelezný, Jirí Kléma, Jakub Tolar, 2011, ISBRA.

Integrating Multiple-Platform Expression Data through Gene Set Features

Filip Zelezný, Jirí Kléma, Jakub Tolar, 2009, ISBRA.

Prediction of DNA-binding proteins from relational features

Filip Železný, Jakub Tolar, Andrea Szabóová, 2012, Proteome Science.

How Computers Discover How Computers Discover ( A mini-review of algorithmic meta-discovery )

F. Železný, 2005 .

Block-wise construction of tree-like relational features with monotone reducibility and redundancy

Ondrej Kuzelka, Filip Zelezný, F. Zelezný, 2011, Machine Learning.

Propositionalization-based relational subgroup discovery with RSD

Nada Lavrac, Filip Zelezný, N. Lavrač, 2006, Machine Learning.

Optimal sports betting strategies in practice: an experimental review pdf

Filip Zelezný, Gustav Sourek, Ondrej Hubácek, 2021, IMA Journal of Management Mathematics.

Learning meets Sequencing : a Generality Framework for Read-Sets

F. Zelezný, J. Tolar, Karel Jalovec, 2015 .

Forty years of score-based soccer match outcome prediction: an experimental review

Ondřej Hubáček, Gustav Šourek, Filip železný, 2021, IMA Journal of Management Mathematics.

Randomised restarted search in ILP

Ashwin Srinivasan, David Page, Filip Zelezný, 2006, Machine Learning.

Ensemble learning of runtime prediction models for gene-expression analysis workflows

David A. Monge, C. Garino, Matej Holec, 2015, Cluster Computing.

Ensemble Learning of Run-Time Prediction Models for Data-Intensive Scientific Workflows

Carlos García Garino, David A. Monge, Filip Zelezný, 2014, CARLA.

Learning Running-time Prediction Models for Gene-Expression Analysis Workflows

Filip Zelezny, David A. Monge, Matej Holec, 2015, IEEE Latin America Transactions.

A Performance Prediction Module for Work ow Scheduling

Carlos García Garino, David A. Monge, Filip Zelezný, 2011 .

Cultural influences on personalized e-learning systems

Scotty D. Craig, Kenneth A. Kaufman, Susanne P. Lajoie, 2012 .

Differential Regulation of the Nuclear Factor-&kgr;B Pathway by Rabbit Antithymocyte Globulins in Kidney Transplantation

F. Zelezný, O. Viklicky, I. Brabcová, 2012, Transplantation.

Prediction of antimicrobial activity of peptides using relational machine learning

Ondrej Kuzelka, Filip Zelezný, Andrea Szabóová, 2012, 2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops.

Learning with Molecules beyond Graph Neural Networks pdf

Gustav Sourek, Filip Zelezny, Ondrej Kuzelka, 2020, ArXiv.

Cross-genome knowledge-based expression data fusion

F. Zelezný, J. Tolar, J. Kléma, 2009 .

Efficiency-conscious propositionalization for relational learning

Filip Zelezný, F. Zelezný, F. Železný, 2004, Kybernetika.

Finding semantic patterns in omics data using concept rule learning with an ontology-based refinement operator

J. Kléma, F. Malinka, F. Železný, 2020, BioData Min..

Lifted Relational Neural Networks pdf

Ondrej Kuzelka, Filip Zelezný, Gustav Sourek, 2015, CoCo@NIPS.

Gene expression data mining guided by genomic background knowledge

Pavel Smrz, Filip Zelezný, Jirí Kléma, 2008, SOFSEM.

An experimental test of Occam’s razor in classification

Filip Zelezný, Jan Zahálka, Jan Zahálka, 2011, Machine Learning.

Efficient Extraction of Network Event Types from NetFlows

Filip Zelezný, Gustav Sourek, Gustav Sourek, 2019, Secur. Commun. Networks.

Evaluating Model-free Directional Dependency Methods on Single-cell RNA Sequencing Data with Severe Dropout

Mingzhou Song, Filip Železný, Karel Drbal, 2019, ICBRA.

Relational Subgroup Discovery for Descriptive Analysis of Microarray Data

Nada Lavrac, Igor Trajkovski, Filip Zelezný, 2006, CompLife.

Relational Descriptive Analysis of Gene Expression Data

Nada Lavrac, Igor Trajkovski, Filip Zelezný, 2006, STAIRS.

Semantic biclustering for finding local, interpretable and predictive expression patterns

F. Zelezný, J. Kléma, F. Malinka, 2017, BMC Genomics.

A method for reduction of examples in relational learning

Ondrej Kuzelka, Filip Zelezný, Andrea Szabóová, 2014, Journal of Intelligent Information Systems.

SumatraTT: Towards a universal data preprocessor

Filip Zelezny, Petr Aubrecht, Olga Stepankova, 2002 .

Estimating Sequence Similarity from Contig Sets

Petr Rysavý, Filip Zelezný, F. Železný, 2017, IDA.

Automating Knowledge Discovery Workflow Composition Through Ontology-Based Planning

Nada Lavrac, Filip Zelezný, Petr Kremen, 2011, IEEE Transactions on Automation Science and Engineering.

ILP Through Propositionalization and Stochastic k-Term DNF Learning

Ashwin Srinivasan, David Page, Gerson Zaverucha, 2007, ILP.

A comparative evaluation of gene set analysis techniques in predictive classification of expression samples

Filip Zelezny, Jakub Tolar, Matej Holec, 2010, BIOINFORMATICS 2010.

Binary classification of metagenomic samples using discriminative DNA superstrings

F. Zelezný, Karel Jalovec, F. Železný, 2014 .

Novel gene sets improve set-level classification of prokaryotic gene expression data

Ondrej Kuzelka, Filip Zelezný, Matej Holec, 2015, BMC Bioinformatics.

Comparative Evaluation of Approaches to Propositionalization

Peter A. Flach, Nada Lavrac, Stefan Wrobel, 2003, ILP.

Extending the ball-histogram method with continuous distributions and an application to prediction of DNA-binding proteins

Ondrej Kuzelka, Filip Zelezný, Andrea Szabóová, 2012, 2012 IEEE International Conference on Bioinformatics and Biomedicine.

Prediction of DNA-binding propensity of proteins by the ball-histogram method using automatic template search

Ondrej Kuzelka, Filip Zelezný, Jakub Tolar, 2011, BMC Bioinformatics.

Mining Frequent Spatial Docking Patterns in Zinc Finger-DNA Complexes

F. Zelezný, J. Tolar, Ondrej Kuzelka, 2010 .

Learning Predictive Categories Using Lifted Relational Neural Networks

Steven Schockaert, Suresh Manandhar, Ondrej Kuzelka, 2016, ILP.