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2020 – today
- 2024
[j41]Edith Heiter
, Robin Vandaele, Tijl De Bie, Yvan Saeys, Jefrey Lijffijt:
Incorporating Topological Priors Into Low-Dimensional Visualizations Through Topological Regularization. IEEE Access 12: 129541-129573 (2024)- [j40]Arne Gevaert, Axel-Jan Rousseau, Thijs Becker, Dirk Valkenborg, Tijl De Bie, Yvan Saeys:
Evaluating feature attribution methods in the image domain. Mach. Learn. 113(9): 6019-6064 (2024) - [j39]Jonathan Peck, Bart Goossens, Yvan Saeys:
An Introduction to Adversarially Robust Deep Learning. IEEE Trans. Pattern Anal. Mach. Intell. 46(4): 2071-2090 (2024) - [c49]Edith Heiter, Liesbet Martens, Ruth Seurinck, Martin Guilliams, Tijl De Bie, Yvan Saeys, Jefrey Lijffijt:
Pattern or Artifact? Interactively Exploring Embedding Quality with TRACE. ECML/PKDD (8) 2024: 379-382 - [i15]Edith Heiter, Liesbet Martens, Ruth Seurinck, Martin Guilliams, Tijl De Bie, Yvan Saeys, Jefrey Lijffijt:
Pattern or Artifact? Interactively Exploring Embedding Quality with TRACE. CoRR abs/2406.12953 (2024) - 2023
- [c48]Arne Gevaert, Anna Saranti, Andreas Holzinger, Yvan Saeys:
Efficient Approximation of Asymmetric Shapley Values Using Functional Decomposition. CD-MAKE 2023: 13-30 - [i14]Edith Heiter, Robin Vandaele, Tijl De Bie, Yvan Saeys, Jefrey Lijffijt:
Topologically Regularized Data Embeddings. CoRR abs/2301.03338 (2023) - [i13]David Novak, Sofie Van Gassen, Yvan Saeys:
GroupEnc: encoder with group loss for global structure preservation. CoRR abs/2309.02917 (2023) - 2022
- [c47]Robin Vandaele, Bo Kang, Tijl De Bie, Yvan Saeys:
The Curse Revisited: When are Distances Informative for the Ground Truth in Noisy High-Dimensional Data? AISTATS 2022: 2158-2172 - [c46]Arne Gevaert, Jonathan Peck, Yvan Saeys:
Distilling Deep RL Models Into Interpretable Neuro-Fuzzy Systems. FUZZ-IEEE 2022: 1-8 - [c45]Robin Vandaele, Bo Kang, Jefrey Lijffijt, Tijl De Bie, Yvan Saeys:
Topologically Regularized Data Embeddings. ICLR 2022 - [c44]Benjamin Rombaut, Joris Roels, Yvan Saeys:
BioSegment: Active Learning segmentation for 3D electron microscopy imaging. IAL@PKDD/ECML 2022: 7-26 - [d2]Arne Gevaert, Axel-Jan Rousseau, Thijs Becker, Dirk Valkenborg, Tijl De Bie, Yvan Saeys:
Evaluating Feature Attribution Methods in the Image Domain: Benchmark results and model parameters. Zenodo, 2022
- [d1]Arne Gevaert, Axel-Jan Rousseau, Thijs Becker, Dirk Valkenborg, Tijl De Bie, Yvan Saeys:
Evaluating Feature Attribution Methods in the Image Domain: High-Dimensional Datasets. Zenodo, 2022 - [i12]Arne Gevaert, Axel-Jan Rousseau, Thijs Becker, Dirk Valkenborg, Tijl De Bie, Yvan Saeys:
Evaluating Feature Attribution Methods in the Image Domain. CoRR abs/2202.12270 (2022) - [i11]Arne Gevaert, Yvan Saeys:
PDD-SHAP: Fast Approximations for Shapley Values using Functional Decomposition. CoRR abs/2208.12595 (2022) - [i10]Arne Gevaert, Jonathan Peck, Yvan Saeys:
Distilling Deep RL Models Into Interpretable Neuro-Fuzzy Systems. CoRR abs/2209.03357 (2022) - 2021
- [j38]Francisco Javier Baldan, Daniel Peralta, Yvan Saeys, José Manuel Benítez:
SCMFTS: Scalable and Distributed Complexity Measures and Features for Univariate and Multivariate Time Series in Big Data Environments. Int. J. Comput. Intell. Syst. 14(1): 186 (2021) - [j37]Robin Vandaele, Bastian Rieck, Yvan Saeys, Tijl De Bie:
Stable topological signatures for metric trees through graph approximations. Pattern Recognit. Lett. 147: 85-92 (2021) - [c43]Daniel Peralta, Lin Tang, Maxim Lippeveld, Yvan Saeys:
A study on the calibration of fingerprint classifiers. IEEE BigData 2021: 698-704 - [i9]Robin Vandaele, Bo Kang, Tijl De Bie, Yvan Saeys:
The Curse Revisited: a Newly Quantified Concept of Meaningful Distances for Learning from High-Dimensional Noisy Data. CoRR abs/2109.10569 (2021) - [i8]Robin Vandaele, Bo Kang, Jefrey Lijffijt, Tijl De Bie, Yvan Saeys:
Topologically Regularized Data Embeddings. CoRR abs/2110.09193 (2021) - 2020
- [j36]Andres Jesus Sanchez, Luis F. Romero, Daniel Peralta, Miguel Angel Medina-Pérez, Yvan Saeys, Francisco Herrera, Siham Tabik:
Asynchronous Processing for Latent Fingerprint Identification on Heterogeneous CPU-GPU Systems. IEEE Access 8: 124236-124253 (2020) - [j35]Daniel Peralta, Yvan Saeys:
Robust unsupervised dimensionality reduction based on feature clustering for single-cell imaging data. Appl. Soft Comput. 93: 106421 (2020) - [j34]Helena Todorov, Robrecht Cannoodt, Wouter Saelens, Yvan Saeys:
TinGa: fast and flexible trajectory inference with Growing Neural Gas. Bioinform. 36(Supplement-1): i66-i74 (2020) - [j33]Jonathan Peck, Bart Goossens, Yvan Saeys:
Detecting adversarial manipulation using inductive Venn-ABERS predictors. Neurocomputing 416: 202-217 (2020) - [j32]Robin Vandaele, Yvan Saeys, Tijl De Bie:
Mining Topological Structure in Graphs through Forest Representations. J. Mach. Learn. Res. 21: 215:1-215:68 (2020) - [c42]Robin Vandaele, Yvan Saeys, Tijl De Bie:
Graph Approximations to Geodesics on Metric Graphs. ICPR 2020: 7328-7334 - [i7]Utku Ozbulak, Jonathan Peck, Wesley De Neve, Bart Goossens, Yvan Saeys, Arnout Van Messem:
Regional Image Perturbation Reduces Lp Norms of Adversarial Examples While Maintaining Model-to-model Transferability. CoRR abs/2007.03198 (2020)
2010 – 2019
- 2019
- [j31]Sarah Vluymans, Neil Mac Parthaláin, Chris Cornelis, Yvan Saeys:
Weight selection strategies for ordered weighted average based fuzzy rough sets. Inf. Sci. 501: 155-171 (2019) - [j30]Pieter Meysman, Yvan Saeys, Ehsan Sabaghian, Wout Bittremieux, Yves Van de Peer, Bart Goethals, Kris Laukens:
Mining the Enriched Subgraphs for Specific Vertices in a Biological Graph. IEEE ACM Trans. Comput. Biol. Bioinform. 16(5): 1496-1507 (2019) - [c41]Daniel Peralta, Yvan Saeys:
Distributed, Numerically Stable Distance and Covariance Computation with MPI for Extremely Large Datasets. BigData Congress 2019: 77-84 - [c40]Arne Gevaert, Jonathan Peck, Yvan Saeys:
Distillation of Deep Reinforcement Learning Models Using Fuzzy Inference Systems. BNAIC/BENELEARN 2019 - [c39]Maxim Lippeveld, Carly Knill, Emma Ladlow, Andrew Fuller, Louise J. Michaelis, Yvan Saeys, Andrew Filby, Daniel Peralta:
Classification of Human White Blood Cells Using Machine Learning for Stain-Free Imaging Flow Cytometry. BNAIC/BENELEARN 2019 - [c38]Jonathan Peck, Bart Goossens, Yvan Saeys:
Calibrated Multi-Probabilistic Prediction as a Defense against Adversarial Attacks. BNAIC/BENELEARN 2019 - [c37]Jonathan Peck, Bart Goossens, Yvan Saeys:
Calibrated Multi-probabilistic Prediction as a Defense Against Adversarial Attacks. BNAIC/BENELEARN (Selected Papers) 2019: 85-125 - [c36]Joris Roels, Yvan Saeys:
Cost-Efficient Segmentation of Electron Microscopy Images Using Active Learning. BNAIC/BENELEARN 2019 - [c35]Jonathan Peck, Bart Goossens, Yvan Saeys:
Detecting adversarial examples with inductive Venn-ABERS predictors. ESANN 2019 - [c34]Joris Roels, Julian Hennies, Yvan Saeys, Wilfried Philips, Anna Kreshuk:
Domain Adaptive Segmentation In Volume Electron Microscopy Imaging. ISBI 2019: 1519-1522 - [i6]Joris Roels, Yvan Saeys:
Cost-efficient segmentation of electron microscopy images using active learning. CoRR abs/1911.05548 (2019) - 2018
- [j29]Jasper Zuallaert, Fréderic Godin, Mijung Kim, Arne Soete, Yvan Saeys, Wesley De Neve:
SpliceRover: interpretable convolutional neural networks for improved splice site prediction. Bioinform. 34(24): 4180-4188 (2018) - [j28]Jasper Zuallaert, Mijung Kim, Arne Soete, Yvan Saeys, Wesley De Neve:
TISRover: ConvNets learn biologically relevant features for effective translation initiation site prediction. Int. J. Data Min. Bioinform. 20(3): 267-284 (2018) - [j27]Daniel Peralta, Isaac Triguero, Salvador García, Yvan Saeys, José Manuel Benítez, Francisco Herrera:
On the use of convolutional neural networks for robust classification of multiple fingerprint captures. Int. J. Intell. Syst. 33(1): 213-230 (2018) - [j26]Sarah Vluymans, Chris Cornelis, Francisco Herrera, Yvan Saeys:
Multi-label classification using a fuzzy rough neighborhood consensus. Inf. Sci. 433-434: 96-114 (2018) - [j25]Sarah Vluymans, Alberto Fernández, Yvan Saeys, Chris Cornelis, Francisco Herrera:
Dynamic affinity-based classification of multi-class imbalanced data with one-versus-one decomposition: a fuzzy rough set approach. Knowl. Inf. Syst. 56(1): 55-84 (2018) - [c33]Robin Vandaele, Tijl De Bie, Yvan Saeys:
Local Topological Data Analysis to Uncover the Global Structure of Data Approaching Graph-Structured Topologies. ECML/PKDD (2) 2018: 19-36 - [i5]Joris Roels, Julian Hennies, Yvan Saeys, Wilfried Philips, Anna Kreshuk:
Domain Adaptive Segmentation in Volume Electron Microscopy Imaging. CoRR abs/1810.09734 (2018) - [i4]Joris Roels, Jonas De Vylder, Jan Aelterman, Yvan Saeys, Wilfried Philips:
Convolutional Neural Network Pruning to Accelerate Membrane Segmentation in Electron Microscopy. CoRR abs/1810.09735 (2018) - [i3]Joris Roels, Jan Aelterman, Jonas De Vylder, Hiep Luong, Yvan Saeys, Wilfried Philips:
Bayesian Deconvolution of Scanning Electron Microscopy Images Using Point-spread Function Estimation and Non-local Regularization. CoRR abs/1810.09739 (2018) - 2017
- [j24]Daniel Peralta, Isaac Triguero, Salvador García, Yvan Saeys, José Manuel Benítez, Francisco Herrera:
Distributed incremental fingerprint identification with reduced database penetration rate using a hierarchical classification based on feature fusion and selection. Knowl. Based Syst. 126: 91-103 (2017) - [j23]Lukasz Kreft, Arne Soete, Paco Hulpiau, Alexander Botzki, Yvan Saeys, Pieter J. De Bleser:
ConTra v3: a tool to identify transcription factor binding sites across species, update 2017. Nucleic Acids Res. 45(Webserver-Issue): W490-W494 (2017) - [c32]Jasper Zuallaert, Mijung Kim, Yvan Saeys, Wesley De Neve:
Interpretable convolutional neural networks for effective translation initiation site prediction. BIBM 2017: 1233-1237 - [c31]Joris Roels, Jonas De Vylder, Jan Aelterman, Yvan Saeys, Wilfried Philips:
Convolutional neural network pruning to accelerate membrane segmentation in electron microscopy. ISBI 2017: 633-637 - [c30]Jonathan Peck, Joris Roels, Bart Goossens, Yvan Saeys:
Lower bounds on the robustness to adversarial perturbations. NIPS 2017: 804-813 - [i2]Daniel Peralta, Isaac Triguero, Salvador García, Yvan Saeys, José Manuel Benítez, Francisco Herrera:
Fingerprint classification with a new deep neural network model: robustness for different captures of the same fingerprints. CoRR abs/1703.07270 (2017) - [i1]Jasper Zuallaert, Mijung Kim, Yvan Saeys, Wesley De Neve:
Interpretable Convolutional Neural Networks for Effective Translation Initiation Site Prediction. CoRR abs/1711.09558 (2017) - 2016
- [j22]Nele Verbiest, Sarah Vluymans, Chris Cornelis, Nicolás García-Pedrajas, Yvan Saeys:
Improving nearest neighbor classification using Ensembles of Evolutionary Generated Prototype Subsets. Appl. Soft Comput. 44: 75-88 (2016) - [j21]Joeri Ruyssinck, Piet Demeester, Tom Dhaene, Yvan Saeys:
Netter: re-ranking gene network inference predictions using structural network properties. BMC Bioinform. 17: 76 (2016) - [j20]Sarah Vluymans, Isaac Triguero, Chris Cornelis, Yvan Saeys:
EPRENNID: An evolutionary prototype reduction based ensemble for nearest neighbor classification of imbalanced data. Neurocomputing 216: 596-610 (2016) - [j19]Sarah Vluymans, Dánel Sánchez Tarragó, Yvan Saeys, Chris Cornelis, Francisco Herrera:
Fuzzy rough classifiers for class imbalanced multi-instance data. Pattern Recognit. 53: 36-45 (2016) - [j18]Sarah Vluymans, Dánel Sánchez Tarragó, Yvan Saeys, Chris Cornelis, Francisco Herrera:
Fuzzy Multi-Instance Classifiers. IEEE Trans. Fuzzy Syst. 24(6): 1395-1409 (2016) - [c29]Joris Roels, Jonas De Vylder, Yvan Saeys, Bart Goossens, Wilfried Philips:
Decreasing Time Consumption of Microscopy Image Segmentation Through Parallel Processing on the GPU. ACIVS 2016: 147-159 - [c28]Joris Roels, Jan Aelterman, Jonas De Vylder, Hiêp Quang Luong, Yvan Saeys, Wilfried Philips:
Bayesian deconvolution of scanning electron microscopy images using point-spread function estimation and non-local regularization. EMBC 2016: 443-447 - [c27]Sarah Vluymans, Neil Mac Parthaláin, Chris Cornelis, Yvan Saeys:
Fuzzy rough sets for self-labelling: An exploratory analysis. FUZZ-IEEE 2016: 931-938 - [c26]Sofie Van Gassen, Tom Dhaene, Yvan Saeys:
Machine Learning Challenges for Single Cell Data. ECML/PKDD (3) 2016: 275-279 - 2015
- [j17]Sarah Vluymans, Lynn D'eer, Yvan Saeys, Chris Cornelis:
Applications of Fuzzy Rough Set Theory in Machine Learning: a Survey. Fundam. Informaticae 142(1-4): 53-86 (2015) - [c25]Isaac Triguero, Mikel Galar, Sarah Vluymans, Chris Cornelis, Humberto Bustince, Francisco Herrera, Yvan Saeys:
Evolutionary undersampling for imbalanced big data classification. CEC 2015: 715-722 - [c24]Leen De Baets, Sofie Van Gassen, Tom Dhaene, Yvan Saeys:
Unsupervised Trajectory Inference Using Graph Mining. CIBB 2015: 84-97 - [c23]Sarah Vluymans, Yvan Saeys, Chris Cornelis, Ankur Teredesai, Martine De Cock:
Fuzzy Rough Set Prototype Selection for Regression. FUZZ-IEEE 2015: 1-8 - [c22]Sarah Vluymans, Hasan Asfoor, Yvan Saeys, Chris Cornelis, Matthew E. Tolentino, Ankur Teredesai, Martine De Cock:
Distributed fuzzy rough prototype selection for Big Data regression. NAFIPS/WConSC 2015: 1-6 - [c21]Richard Jensen, Sarah Vluymans, Neil Mac Parthaláin, Chris Cornelis, Yvan Saeys:
Semi-Supervised Fuzzy-Rough Feature Selection. RSFDGrC 2015: 185-195 - 2014
- [c20]Celine Vens, Sofie Van Gassen, Tom Dhaene, Yvan Saeys:
Complex Aggregates over Clusters of Elements. ILP 2014: 181-193 - [c19]Joris Roels, Jan Aelterman, Jonas De Vylder, Hiêp Quang Luong, Yvan Saeys, Saskia Lippens, Wilfried Philips:
Noise Analysis and Removal in 3D Electron Microscopy. ISVC (1) 2014: 31-40 - 2012
- [j16]Vân Anh Huynh-Thu, Yvan Saeys, Louis Wehenkel, Pierre Geurts:
Statistical interpretation of machine learning-based feature importance scores for biomarker discovery. Bioinform. 28(13): 1766-1774 (2012) - [c18]Dieter Mourisse, Els Lefever, Nele Verbiest, Yvan Saeys, Martine De Cock, Chris Cornelis:
SBFC: An Efficient Feature Frequency-Based Approach to Tackle Cross-Lingual Word Sense Disambiguation. TSD 2012: 248-255 - 2011
- [j15]Jan Fostier, Sebastian Proost, Bart Dhoedt, Yvan Saeys, Piet Demeester, Yves Van de Peer, Klaas Vandepoele:
A greedy, graph-based algorithm for the alignment of multiple homologous gene lists. Bioinform. 27(6): 749-756 (2011) - [j14]Sofie Van Landeghem, Bernard De Baets, Yves Van de Peer, Yvan Saeys:
High-Precision Bio-molecular Event Extraction from Text Using Parallel Binary Classifiers. Comput. Intell. 27(4): 645-664 (2011) - [j13]Rubén Armañanzas, Yvan Saeys, Iñaki Inza, Miguel García-Torres, Concha Bielza, Yves Van de Peer, Pedro Larrañaga:
Peakbin Selection in Mass Spectrometry Data Using a Consensus Approach with Estimation of Distribution Algorithms. IEEE ACM Trans. Comput. Biol. Bioinform. 8(3): 760-774 (2011) - [c17]Huu Minh Nguyen, Ivo Couckuyt, Luc Knockaert, Tom Dhaene, Dirk Gorissen, Yvan Saeys:
An alternative approach to avoid overfitting for surrogate models. WSC 2011: 2765-2776 - 2010
- [j12]Thomas Abeel, Thibault Helleputte, Yves Van de Peer, Pierre Dupont, Yvan Saeys:
Robust biomarker identification for cancer diagnosis with ensemble feature selection methods. Bioinform. 26(3): 392-398 (2010) - [j11]Sofie Van Landeghem, Thomas Abeel, Yvan Saeys, Yves Van de Peer:
Discriminative and informative features for biomolecular text mining with ensemble feature selection. Bioinform. 26(18) (2010) - [j10]Thomas Abeel, Sofie Van Landeghem, Roser Morante, Vincent Van Asch, Yves Van de Peer, Walter Daelemans, Yvan Saeys:
Highlights of the BioTM 2010 workshop on advances in bio text mining. BMC Bioinform. 11(S-5): I1 (2010) - [c16]Catherine Middag, Yvan Saeys, Jean-Pierre Martens:
Towards an ASR-free objective analysis of pathological speech. INTERSPEECH 2010: 294-297 - [c15]Yvan Saeys, Sofie Van Landeghem, Yves Van de Peer:
Event based text mining for integrated network construction. MLSB 2010: 112-121
2000 – 2009
- 2009
- [j9]Thomas Abeel, Yves Van de Peer, Yvan Saeys:
Toward a gold standard for promoter prediction evaluation. Bioinform. 25(12) (2009) - [j8]Thomas Abeel, Yves Van de Peer, Yvan Saeys:
Java-ML: A Machine Learning Library. J. Mach. Learn. Res. 10: 931-934 (2009) - [c14]Sofie Van Landeghem, Yvan Saeys, Bernard De Baets, Yves Van de Peer:
Analyzing text in search of bio-molecular events: a high-precision machine learning framework. BioNLP@HLT-NAACL (Shared Task) 2009: 128-136 - [c13]Nele Verbiest, Chris Cornelis, Yvan Saeys:
Valued Constraint Satisfaction Problems Applied to Functional Harmony. IFSA/EUSFLAT Conf. 2009: 925-930 - 2008
- [j7]Rubén Armañanzas, Iñaki Inza, Roberto Santana, Yvan Saeys, Jose Luis Flores, José Antonio Lozano, Yves Van de Peer, Rosa Blanco, Víctor Robles, Concha Bielza, Pedro Larrañaga:
A review of estimation of distribution algorithms in bioinformatics. BioData Min. 1 (2008) - [j6]Michiel Van Bel, Yvan Saeys, Yves Van de Peer:
FunSiP: a modular and extensible classifier for the prediction of functional sites in DNA. Bioinform. 24(13): 1532-1533 (2008) - [c12]Thomas Abeel, Yvan Saeys, Pierre Rouzé, Yves Van de Peer:
ProSOM: core promoter prediction based on unsupervised clustering of DNA physical profiles. ISMB 2008: 24-31 - [c11]Isis Bonet, Abdel Rodríguez, Ricardo Grau Ábalo, María M. García, Yvan Saeys, Ann Nowé:
Comparing Distance Measures with Visual Methods. MICAI 2008: 90-99 - [c10]Yvan Saeys, Thomas Abeel, Yves Van de Peer:
Robust Feature Selection Using Ensemble Feature Selection Techniques. ECML/PKDD (2) 2008: 313-325 - [c9]Yvan Saeys, Huan Liu, Iñaki Inza, Louis Wehenkel, Yves Van de Peer:
Preface. FSDM 2008: 1-4 - [e2]Yvan Saeys, Huan Liu, Iñaki Inza, Louis Wehenkel, Yves Van de Peer:
Third Workshop on New Challenges for Feature Selection in Data Mining and Knowledge Discovery, FSDM 2008, held at ECML-PKDD 2008, Antwerp, Belgium, September 15, 2008. JMLR Proceedings 4, JMLR.org 2008 [contents] - 2007
- [j5]Yvan Saeys, Pierre Rouzé, Yves Van de Peer:
In search of the small ones: improved prediction of short exons in vertebrates, plants, fungi and protists. Bioinform. 23(4): 414-420 (2007) - [j4]Yvan Saeys, Iñaki Inza, Pedro Larrañaga:
A review of feature selection techniques in bioinformatics. Bioinform. 23(19): 2507-2517 (2007) - [j3]Tom Michoel, Steven Maere, Eric Bonnet, Anagha Joshi, Yvan Saeys, Tim Van den Bulcke, Koenraad Van Leemput, Piet van Remortel, Martin Kuiper, Kathleen Marchal, Yves Van de Peer:
Validating module network learning algorithms using simulated data. BMC Bioinform. 8(S-2) (2007) - [c8]Yvan Saeys, Thomas Abeel, Sven Degroeve, Yves Van de Peer:
Translation initiation site prediction on a genomic scale: beauty in simplicity. ISMB/ECCB (Supplement of Bioinformatics) 2007: 418-423 - [c7]Isis Bonet, María M. García, Yvan Saeys, Yves Van de Peer, Ricardo Grau Ábalo:
Predicting Human Immunodeficiency Virus (HIV) Drug Resistance Using Recurrent Neural Networks. IWINAC (1) 2007: 234-243 - [e1]Karl Tuyls, Ronald L. Westra, Yvan Saeys, Ann Nowé:
Knowledge Discovery and Emergent Complexity in Bioinformatics, First International Workshop, KDECB 2006, Ghent, Belgium, May 10, 2006. Revised Selected Papers. Lecture Notes in Computer Science 4366, Springer 2007, ISBN 978-3-540-71036-3 [contents] - 2006
- [c6]Isis Bonet, Yvan Saeys, Ricardo Grau Ábalo, María M. García, Robersy Sanchez, Yves Van de Peer:
Feature Extraction Using Clustering of Protein. CIARP 2006: 614-623 - [c5]Philippe Faes, Bram Minnaert, Mark Christiaens, Eric Bonnet, Yvan Saeys, Dirk Stroobandt, Yves Van de Peer:
Scalable hardware accelerator for comparing DNA and protein sequences. Infoscale 2006: 33 - [c4]Ronald L. Westra, Karl Tuyls, Yvan Saeys, Ann Nowé:
Knowledge Discovery and Emergent Complexity in Bioinformatics. KDECB 2006: 1-9 - [c3]Yvan Saeys, Yves Van de Peer:
Enhancing Coding Potential Prediction for Short Sequences Using Complementary Sequence Features and Feature Selection. KDECB 2006: 107-118 - [p1]Yvan Saeys, Sven Degroeve, Yves Van de Peer:
Feature Ranking Using an EDA-based Wrapper Approach. Towards a New Evolutionary Computation 2006: 243-257 - 2005
- [j2]Sven Degroeve, Yvan Saeys, Bernard De Baets, Pierre Rouzé, Yves Van de Peer:
SpliceMachine: predicting splice sites from high-dimensional local context representations. Bioinform. 21(8): 1332-1338 (2005) - 2004
- [j1]Yvan Saeys, Sven Degroeve, Dirk Aeyels, Pierre Rouzé, Yves Van de Peer:
Feature selection for splice site prediction: A new method using EDA-based feature ranking. BMC Bioinform. 5: 64 (2004) - [c2]Yvan Saeys, Sven Degroeve, Yves Van de Peer:
Digging into Acceptor Splice Site Prediction: An Iterative Feature Selection Approach. PKDD 2004: 386-397 - 2003
- [c1]Yvan Saeys, Sven Degroeve, Dirk Aeyels, Yves Van de Peer, Pierre Rouzé:
Fast feature selection using a simple estimation of distribution algorithm: a case study on splice site prediction. ECCB 2003: 179-188
Coauthor Index
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