drug–target binary associations. The resulting network connects most drugs into a highly interlinked giant component, with strong local clustering of drugs of similar types according to Anatomical Therapeutic Chemical classification. Topological analyses of this network quantitatively showed an overabundance ...
Next, we turn to quantify the network-based relationship between two drug–target modules and a disease module (drug–drug–disease combinations). We find that from a network perspective, all possible drug–drug–disease combinations can be classified into six topologically distinct classes: (a) Ov...
Concurrently, network representation learning D jeddi et al. BMC Bioinformatics (2023) 24:488 Page 5 of 42 methods have emerged as a vital component in this endeavor. These methods can be broadly classified into three categories: matrix factorization-based, random walk-based, and neural ...
We built a bipartite graph composed of US Food and Drug Administration-approved drugs and proteins linked by drug-target binary associations. The resulting network connects most drugs into a highly interlinked giant component, with strong local clustering of drugs of similar types according to ...
This is a heterogeneous drug-target and drug–drug interaction network Full size image Figure 1 shows how a drug discovery problem can be converted to a link prediction problem. The relationship network is heterogeneous as many entities are related, such as drug–drug, drug–gene, drug–disease...
Intracellular disease models refer to the modelling of disease-associated changes in individual cell types. For this purpose, we use a network-based approach, mapping disease-associated DEGs onto the PPIN. Using the largest connected component formed by a cell types DEGs in the PPIN, we can then...
Ligand-conjugated polymeric micelles which target specific receptors on cells have been developed and applied for many disease diagnosis/treatment. Polymers are an attractive material for drug delivery because they are extraordinarily malleable and moldable for particles’ sizes and shapes. Moreover, it ...
Intuitively, the low-dimensional feature vector obtained from compact feature learning encodes the relational properties (e.g., similarity), association information and topological context of each drug (or protein) in the heterogeneous network. Akin to principal component analysis, which seeks the ...
Drug response differs substantially in cancer patients due to inter- and intra-tumor heterogeneity. Particularly, transcriptome context, especially tumor microenvironment, has been shown playing a significant role in shaping the actual treatment outcome.
We built a bipartite graph composed of US Food and Drug Administration-approved drugs and proteins linked by drug-target binary associations. The resulting network connects most drugs into a highly interlinked giant component, with strong local clustering of drugs of similar types according to ...