Dcomg: Drug Combination Prediction by Applying Gnns on DDI Node2vec Features

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Ziaee Seyyed Sina; RAHMANI HOSSEIN; Tabatabaei Mina

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Seminar Paper

Paper Information

Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2022 | Issue:8

Download Full-Text

View:

144

Download:

Information Seminar Paper

Title

Dcomg: Drug Combination Prediction by Applying Gnns on DDI Node2vec Features

Author(s)

Ziaee Seyyed Sina | RAHMANI HOSSEIN | Tabatabaei Mina

Keywords

Datamining

Graph Neural Network

Drug Combination

Drug-Drug Interaction

Node2vec

PCA

Graph

Abstract

Recent studies have been indicating that many clinical Drug Combinations surpass single-drug therapy efficacy. Machine learning, deep learning, network analysis, and search algorithms have been considered to facilitate the discovery of synergistic Drug Combinations, and two of the best state-of-the-art models in this area are under the deep learning category. In this paper, we present DComG, a Graph Auto Encoder method to predict synergistic Drug Combinations. Using the dataset provided in DCDB, our analysis shows tremendous improvement in the performance of predicting new Drug Combinations over previously introduced state-of-the-art models by an average of 4% in ROC_AUC scores. We highlight the importance of Drug-Drug Interactions (DDI) in the form of Node2vec features of DComG Graph inputs for predicting new Drug Combinations. Finally, we address the results of our model in terms of biological interpretations of Drug Combinations based on recent medical Drug Combination papers in the literature.

Video

Cites

No record.

References

No record.

Cite

APA: Copy

Ziaee, Seyyed Sina, RAHMANI, HOSSEIN, & Tabatabaei, Mina. (2022). Dcomg: Drug Combination Prediction by Applying Gnns on DDI Node2vec Features. INTERNATIONAL CONFERENCE ON WEB RESEARCH. SID. https://sid.ir/paper/949619/en

Vancouver: Copy

Ziaee Seyyed Sina, RAHMANI HOSSEIN, Tabatabaei Mina. Dcomg: Drug Combination Prediction by Applying Gnns on DDI Node2vec Features. 2022. Available from: https://sid.ir/paper/949619/en

IEEE: Copy

Seyyed Sina Ziaee, HOSSEIN RAHMANI, and Mina Tabatabaei, “Dcomg: Drug Combination Prediction by Applying Gnns on DDI Node2vec Features,” presented at the INTERNATIONAL CONFERENCE ON WEB RESEARCH. 2022, [Online]. Available: https://sid.ir/paper/949619/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops