Journal of Advances in Information Technology (JAIT)
@article{AltartouriGlasmachers2021,
author = {Altartouri, Haneen and Glasmachers, Tobias},
title = {Improved Protein Function Prediction by Combining Clustering with Ensemble Classification},
journal = {Journal of Advances in Information Technology (JAIT)},
month = {August},
year = {2021},
}
Altartouri, H., & Glasmachers, T.. (2021). Improved Protein Function Prediction by Combining Clustering with Ensemble Classification. Journal of Advances in Information Technology (JAIT).
2020
A Versatile Combination of Classifiers for Protein Function Prediction
The Twelfth International Conference on Bioinformatics, Biocomputational Systems and Biotechnologies
@aricle{AltartouriGlasmachers2020,
author = {Altartouri, Haneen and Glasmachers, Tobias},
title = {A Versatile Combination of Classifiers for Protein Function Prediction},
year = {2020},
}
Altartouri, H., & Glasmachers, T.. (2020). A Versatile Combination of Classifiers for Protein Function Prediction. The Twelfth International Conference on Bioinformatics, Biocomputational Systems and Biotechnologies.
2019
Moment Vector Encoding of Protein Sequences for Supervised Classification
In Practical Applications of Computational Biology and Bioinformatics, 13th International Conference (pp. 25–35) Springer International Publishing
@incollection{AltartouriGlasmachers2019,
author = {Altartouri, Haneen and Glasmachers, Tobias},
title = {Moment Vector Encoding of Protein Sequences for Supervised Classification},
booktitle = {Practical Applications of Computational Biology and Bioinformatics, 13th International Conference},
pages = {25–35},
publisher = {Springer International Publishing},
month = {June},
year = {2019},
doi = {10.1007/978-3-030-23873-5_4},
}
Altartouri, H., & Glasmachers, T.. (2019). Moment Vector Encoding of Protein Sequences for Supervised Classification. In Practical Applications of Computational Biology and Bioinformatics, 13th International Conference (pp. 25–35). Springer International Publishing. http://doi.org/10.1007/978-3-030-23873-5_4
The Institut für Neuroinformatik (INI) is a central research unit of the
Ruhr-Universität Bochum. We aim to understand the fundamental principles
through which organisms generate behavior and cognition while linked to
their environments through sensory systems and while acting in those
environments through effector systems. Inspired by our insights into
such natural cognitive systems, we seek new solutions to problems of
information processing in artificial cognitive systems. We draw from a
variety of disciplines that include experimental approaches from
psychology and neurophysiology as well as theoretical approaches from
physics, mathematics, electrical engineering and applied computer
science, in particular machine learning, artificial intelligence, and
computer vision.