Wesley De Neve
Wesley De Neve received the M.Sc. degree in Computer Science and the Ph.D. degree in Computer Science Engineering from Ghent University, Ghent, Belgium, in 2002 and 2007, respectively. He is currently working as an Associate Professor for both the IDLab at Ghent University in Belgium and the Center for Biotech Data Science at the Ghent University Global Campus (GUGC) in Korea. His teaching responsibilities are in the areas of Informatics (scientific problem solving using Python and UNIX) and Bioinformatics (algorithmic design and analysis at the intersection of computer science and biology). His research focuses on the development of novel compression and machine learning approaches towards the representation and analysis of multimedia content (that is, text and video) and biotech data (that is, omics data and medical images).
Keywords: Biotech, Deep learning, Machine learning, Genomic data analysis, Multimedia content analysis
Jasper Zuallaert, Fréderic Godin, Mijung Kim, Arne Soete, Yvan Saeys, and Wesley De Neve. 2018. “SpliceRover: Interpretable Convolutional Neural Networks for Improved Splice Site Prediction.” Bioinformatics. Accepted for publication.
Fréderic Godin, Jonas Degrave, Joni Dambre, and Wesley De Neve. 2018. “Dual Rectified Linear Units (DReLUs): A Replacement for Tanh Activation Functions in Quasi-Recurrent Neural Networks.” Pattern Recognition Letters. Accepted for publication.
Jasper Zuallaert, Mijung Kim, Arne Soete, Yvan Saeys, and Wesley De Neve. 2018. “TISRover: ConvNets Learn Biologically Relevant Features for Effective Translation Initiation Site Prediction.” International Journal of Data Mining and Bioinformatics 20 (3): 267–284.
Fréderic Godin, Viktor Slavkovikj, Wesley De Neve, Benjamin Schrauwen, and Rik Van de Walle. 2013. “Using topic models for Twitter hashtag recommendation.” Proceedings of the 22nd International Conference on World Wide Web: 593-596.
Jae Young Choi, Wesley De Neve, Konstantinos N Plataniotis, and Yong Man Ro. 2011. “Collaborative face recognition for improved face annotation in personal photo collections shared on online social networks.” IEEE Transactions on Multimedia 13 (1): 14-28.