A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
A comparison of embedding aggregation strategies in drug-target interaction prediction
Tekijät: Iliadis Dimitrios, De Baets Bernard, Pahikkala Tapio, Waegeman Willem
Kustantaja: BMC
Julkaisuvuosi: 2024
Journal: BMC Bioinformatics
Tietokannassa oleva lehden nimi: BMC BIOINFORMATICS
Lehden akronyymi: BMC BIOINFORMATICS
Artikkelin numero: 59
Vuosikerta: 25
Numero: 1
Sivujen määrä: 18
ISSN: 1471-2105
eISSN: 1471-2105
DOI: https://doi.org/10.1186/s12859-024-05684-y
Verkko-osoite: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-024-05684-y
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/386948179
The prediction of interactions between novel drugs and biological targets is a vital step in the early stage of the drug discovery pipeline. Many deep learning approaches have been proposed over the last decade, with a substantial fraction of them sharing the same underlying two-branch architecture. Their distinction is limited to the use of different types of feature representations and branches (multi-layer perceptrons, convolutional neural networks, graph neural networks and transformers). In contrast, the strategy used to combine the outputs (embeddings) of the branches has remained mostly the same. The same general architecture has also been used extensively in the area of recommender systems, where the choice of an aggregation strategy is still an open question. In this work, we investigate the effectiveness of three different embedding aggregation strategies in the area of drug-target interaction (DTI) prediction. We formally define these strategies and prove their universal approximator capabilities. We then present experiments that compare the different strategies on benchmark datasets from the area of DTI prediction, showcasing conditions under which specific strategies could be the obvious choice.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
