Convolutional dynamic auto-encoder: a clustering method for semantic images

Mohamed, Zahra; Ksantini, Riadh; Kaabi, Jihene

doi:10.1007/s00521-022-07359-2

Cited by 3 publications

(1 citation statement)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, MultiDTI ( Zhou et al 2021 ) reduces the feature dimensions of targets and drugs adjusting the size of convolution kernel in a deep convolutional neural network. One should note that the reducing auto-encoder strategy could induce information loss when disposing of redundant information ( Mohamed et al 2022 ).…”

Section: Methodsmentioning

confidence: 99%

iGRLDTI: an improved graph representation learning method for predicting drug–target interactions over heterogeneous biological information network

Zhao,

Su,

et al. 2023

Bioinformatics

View full text Add to dashboard Cite

Motivation The task of predicting drug-target interactions (DTIs) plays a significant role in faciliating the development of novel drug discovery. Compared with laboratory-based approaches, computational methods proposed for DTI prediction are preferred due to their high-efficiency and low-cost advantages. Recently, much attention has been attracted to apply different graph neural network (GNN) models to discover underlying DTIs from hetergeneous biological information network (HBIN). Although GNN-based prediction methods achieve better performance, they are prone to encounter the over-smoothing simulation when learning the latent representations of drugs and targets with their rich neighborhood information in HBIN, and thereby reduce the discriminative ability in DTI prediction. Results In this work, an improved graph representation learning method, namely iGRLDTI, is proposed to address the above issue by better capturing more discriminative representations of drugs and targets in a latent feature space. Specifically, iGRLDTI first constructs a HBIN by integrating the biological knowledge of drugs and targets with their interactions. After that, it adopts a node-dependent local smoothing strategy to adaptively decide the propagation depth of each biomolecule in HBIN, thus significantly alleviating over-smoothing by enhancing the discriminative ability of feature represeantions of drugs and targets. Finally, a Gradient Boosting Decision Tree classifier is used by iGRLDTI to predict novel DTIs. Experimental results demonstrate that iGRLDTI yields better performance that several state-of-the-art computational methods on the benchmark dataset. Besides, our case study indicates that iGRLDTI can successfully identify novel DTIs with more distinguishable features of drugs and targets. Availability Python codes and dataset are available at https://github.com/stevejobws/iGRLDTI/. Supplementary information Supplementary data are available at Bioinformatics online.

show abstract

Section: Methodsmentioning

confidence: 99%