GraphMS: Drug Target Prediction Using Graph Representation Learning with Substructures
The prediction of drug–target interactions is always a key task in the field of drug redirection. However, traditional methods of predicting drug–target interactions are either mediocre or rely heavily on data stacking. In this work, we proposed our model named GraphMS. We merged heterogeneous graph...
Main Authors: | Shicheng Cheng, Liang Zhang, Bo Jin, Qiang Zhang, Xinjiang Lu, Mao You, Xueqing Tian |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-04-01
|
Series: | Applied Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-3417/11/7/3239 |
Similar Items
-
Representing Graphs as Bag of Vertices and Partitions for Graph Classification
by: Mansurul Bhuiyan, et al.
Published: (2018-06-01) -
An Efficient Subgraph Isomorphism Solver for Large Graphs
by: Zubair Ali Ansari, et al.
Published: (2021-01-01) -
Link Prediction Based on Orbit Counting and Graph Auto-Encoder
by: Jian Feng, et al.
Published: (2020-01-01) -
Reliable Knowledge Graph Path Representation Learning
by: Seungmin Seo, et al.
Published: (2020-01-01) -
On Training Knowledge Graph Embedding Models
by: Sameh K. Mohamed, et al.
Published: (2021-03-01)