Stock Michiel

michiel.stock@ugent.be
@KERMIT, office 110.54
(+32) 9 264.60.18

linkedin logo Stock Michiel
ORCID logo https://orcid.org/0000-0003-0903-6061
Biblio logoBiblio UGent
Google Scholar logoGoogle Scholar
Twitter #michielstock
Twitterhttps://michielstock.github.io/

Research Interests:

Machine learning methods for understanding, predicting and controlling community ecosystems.

Learning from networks

Journal papers

Biblio logo(18) Information content in pollination network reveals missing interactions
M. Stock, N. Piot, S. Vanbesien, B. Vaissière, C. Coiffait-Gombault, G. Smagghe and B. De Baets
(2020) ECOLOGICAL MODELLING. 431, 109161.
Biblio logo(17) Gloxinia - An open-source sensing platform to monitor the dynamic responses of plants
O. Pieters, T. De Swaef, P. Lootens, M. Stock, I. Roldán-Ruiz and F. wyffels
(2020) SENSORS. 20, 3055.
Biblio logo(16) Predicting pharmaceutical particle size distributions using kernel mean embedding
D. Van Hauwermeiren, M. Stock, T. De Beer and I. Nopens
(2020) PHARMACEUTICS. 12, 271.
Biblio logo(15) Algebraic shortcuts for leave-one-out cross-validation in supervised network inference
M. Stock, T. Pahikkala, A. Airola, W. Waegeman and B. De Baets
(2020) BRIEFINGS IN BIOINFORMATICS. 21, 262-271.
Biblio logo(14) X-ray microtomography and linear discriminant analysis enable extraction of embolism-related acoustic emissions
N.J.F. De Baerdemaker, M. Stock, J. Van Den Bulcke, B. De Baets, L. Van Hoorebeke and K. Steppe
(2019) PLANT METHODS. 15, 153.
Biblio logo(13) Guiding mineralization co-culture discovery using Bayesian optimization
A.J. Daly, M. Stock, J.M. Baetens and B. De Baets
(2019) ENVIRONMENTAL SCIENCE AND TECHNOLOGY. 53, 14459-14469.
Biblio logo(12) EcologicalNetworks.jl: analysing ecological networks of species interactions
T. Poisot, Z. Bélisle, L. Hoebeke, M. Stock and P. Szefer
(2019) ECOGRAPHY. 42, 1850-1861.
Biblio logo(11) A comparative study of pairwise learning methods based on Kernel Ridge Regression
M. Stock, T. Pahikkala, A. Airola, B. De Baets and W. Waegeman
(2018) NEURAL COMPUTATION. 30, 2245-2283.
Biblio logo(10) miSTAR: miRNA target prediction through modeling quantitative and qualitative miRNA binding site information in a stacked model structure
G. 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.
Biblio logo(9) Linear filtering reveals false negatives in species interaction data
M. Stock, T. Poisot, W. Waegeman and B. De Baets
(2017) SCIENTIFIC REPORTS. 7, 45908.
Biblio logo(8) Exact and efficient top-K inference for multi-target prediction by querying separable linear relational models
M. Stock, K. Dembczyński, B. De Baets and W. Waegeman
(2016) DATA MINING AND KNOWLEDGE DISCOVERY. 30, 1370-1394.
Biblio logo(7) Data-driven recipe completion using machine learning methods
M. De Clercq, M. Stock, B. De Baets and W. Waegeman
(2016) TRENDS IN FOOD SCIENCE & TECHNOLOGY. 49, 1-13.
Biblio logo(6) A decoy-free approach to the identification of peptides
G. Gonnelli, M. Stock, J. Verwaeren, D. Maddelein, B. De Baets, L. Martens and S. Degroeve
(2015) JOURNAL OF PROTEOME RESEARCH. 14, 1792-1798.
Biblio logo(5) A community effort to assess and improve drug sensitivity prediction algorithms
J.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.
Biblio logo(4) Identification of functionally-related enzymes by learning-to-rank methods and cavity-based similarity measures
M. 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.
Biblio logo(3) Exploration and prediction of interactions between methanotrophs and heterotrophs
M. 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.
Biblio logo(2) Efficient regularized least-squares algorithms for conditional ranking on relational data
T. Pahikkala, A. Airola, M. Stock, B. De Baets and W. Waegeman
(2013) MACHINE LEARNING. 93, 321-356.
Biblio logo(1) A kernel-based framework for learning graded relations from data
W. Waegeman, T. Pahikkala, A. Airola, T. Salakoski, M. Stock and B. De Baets
(2012) IEEE TRANSACTIONS ON FUZZY SYSTEMS. 20, 1090-1101.