 (17) Gloxinia - An open-source sensing platform to monitor the dynamic responses of plantsO. Pieters, T. De Swaef, P. Lootens, M. Stock, I. Roldán-Ruiz and F. wyffels(2020) SENSORS. 20, 3055. |
| (16) Predicting pharmaceutical particle size distributions using kernel mean embeddingD. Van Hauwermeiren, M. Stock, T. De Beer and I. Nopens(2020) PHARMACEUTICS. 12, 271. |
| (15) Algebraic shortcuts for leave-one-out cross-validation in supervised network inferenceM. Stock, T. Pahikkala, A. Airola, W. Waegeman and B. De Baets(2020) BRIEFINGS IN BIOINFORMATICS. 21, 262-271. |
| (14) X-ray microtomography and linear discriminant analysis enable extraction of embolism-related acoustic emissionsN.J.F. De Baerdemaker, M. Stock, J. Van Den Bulcke, B. De Baets, L. Van Hoorebeke and K. Steppe(2019) PLANT METHODS. 15, 153. |
| (13) Guiding mineralization co-culture discovery using Bayesian optimizationA.J. Daly, M. Stock, J.M. Baetens and B. De Baets(2019) ENVIRONMENTAL SCIENCE AND TECHNOLOGY. 53, 14459-14469. |
| (12) EcologicalNetworks.jl: analysing ecological networks of species interactionsT. Poisot, Z. Bélisle, L. Hoebeke, M. Stock and P. Szefer(2019) ECOGRAPHY. 42, 1850-1861. |
| (11) A comparative study of pairwise learning methods based on Kernel Ridge RegressionM. Stock, T. Pahikkala, A. Airola, B. De Baets and W. Waegeman(2018) NEURAL COMPUTATION. 30, 2245-2283. |
| (10) miSTAR: miRNA target prediction through modeling quantitative and qualitative miRNA binding site information in a stacked model structureG. Van Peer, A. De Paepe, M. Stock, J. Anckaert, P-J. Volders, J. Vandesompele, B. De Baets and W. Waegeman(2017) NUCLEIC ACIDS RESEARCH. 45, e51. |
| (9) Linear filtering reveals false negatives in species interaction dataM. Stock, T. Poisot, W. Waegeman and B. De Baets(2017) SCIENTIFIC REPORTS. 7, 45908. |
| (8) Exact and efficient top-K inference for multi-target prediction by querying separable linear relational modelsM. Stock, K. Dembczyński, B. De Baets and W. Waegeman(2016) DATA MINING AND KNOWLEDGE DISCOVERY. 30, 1370-1394. |
| (7) Data-driven recipe completion using machine learning methodsM. De Clercq, M. Stock, B. De Baets and W. Waegeman(2016) TRENDS IN FOOD SCIENCE & TECHNOLOGY. 49, 1-13. |
| (6) A decoy-free approach to the identification of peptidesG. Gonnelli, M. Stock, J. Verwaeren, D. Maddelein, B. De Baets, L. Martens and S. Degroeve(2015) JOURNAL OF PROTEOME RESEARCH. 14, 1792-1798. |
| (5) A community effort to assess and improve drug sensitivity prediction algorithmsJ.C. Costello, L.M. Heiser, E. Georgii, M. Gönen, M.P. Menden, N.J. Wang, M. Bansal, M. Ammad-ud-din, P. Hintsanen, S.A. Khan, J.-P. Mpindi, O. Kallioniemi, A. Honkela, T. Aittokallio, K. Wennerberg, J.-P. Abbuehl, J. Allen, R.B. Altman, S. Balcome, A. Battle, A. Bender, B. Berger, J. Bernard, M. Bhattacharjee, K. Bhuvaneshwar, A.A. Bieberich, F. Boehm, A. Califano, C. Chan, B. Chen, T.-H. Chen, J. Choi, L.P. Coelho, T. Cokelaer, C.J. Creighton, J. Cui, W. Dampier, V.J. Davisson, B. De Baets, R. Deshpande, B. DiCamillo, M. Dundar, Z. Duren, A. Ertel, H. Fan, H. Fang, R. Gauba, A. Gottlieb, M. Grau, Y. Gusev, M.J. Ha, L. Han, M. Harris, N. Henderson, H.A. Hejase, K. Homicsko, J.P. Hou, W. Hwang, A.P. Ijzerman, B. Karacali, S. Keles, C. Kendziorski, J. Kim, M. Kim, Y. Kim, D.A. Knowles, D. Koller, J. Lee, J.K. Lee, E.B. Lenselink, B. Li, B. Li, J. Li, H. Liang, J. Ma, S. Madhavan, S. Mooney, C.L. Myers, M.A Newton, J.P. Overington, R. Pal, J. Peng, R. Pestell, R.J. Prill, P. Qiu, B. Rajwa, A. Sadanandam, F. Sambo, H. Shin, J. Song, L. Song, A. Sridhar, M. Stock, W. Sun, T. Ta, M. Tadesse, M. Tan, H. Tang, D. Theodorescu, G.M. Toffolo, A. Tozeren, W. Trepicchio, N. Varoquaux, J.-P. Vert, W. Waegeman, T. Walter, Q. Wan, D. Wang, W. Wang, Y. Wang, Z. Wang, J.K. Wegner, T. Wu, T. Xia, G. Xiao, Y. Xie, Y. Xu, J. Yang, Y. Yuan, S. Zhang, X.-S. Zhang, J. Zhao, C. Zuo, H.W.T. van Vlijmen, G.J.P. van Westen, J.J. Collins, D. Gallahan, D. Singer, J. Saez-Rodriguez, S. Kaski, J.W. Gray and G. Stolovitzky(2014) NATURE BIOTECHNOLOGY. 32, 1202-1212. |
| (4) Identification of functionally-related enzymes by learning-to-rank methods and cavity-based similarity measuresM. Stock, T. Fober, E. Hüllermeier, S. Glinca, G. Klebe, T. Pahikkala, A. Airola, B. De Baets and W. Waegeman(2014) IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS. 11, 1157-1169. |
| (3) Exploration and prediction of interactions between methanotrophs and heterotrophsM. Stock, S. Hoefman, F.-M. Kerckhof, N. Boon, P. De Vos, B. De Baets, K. Heylen and W. Waegeman(2013) RESEARCH IN MICROBIOLOGY. 10, 1045-1054. |
| (2) Efficient regularized least-squares algorithms for conditional ranking on relational dataT. Pahikkala, A. Airola, M. Stock, B. De Baets and W. Waegeman(2013) MACHINE LEARNING. 93, 321-356. |
| (1) A kernel-based framework for learning graded relations from dataW. Waegeman, T. Pahikkala, A. Airola, T. Salakoski, M. Stock and B. De Baets(2012) IEEE TRANSACTIONS ON FUZZY SYSTEMS. 20, 1090-1101. |