Drug-Disease Association and Drug-Repositioning Predictions in Complex Diseases Using Causal Inference-Probabilistic Matrix Factorization

By Jihong Yang, Zheng Li, Xiaohui Fan, Yiyu Cheng. 2014

91 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Zhiyong Lu
2. Srinivasan Parthasarathy
3. Atul J. Butte
4. Ping Zhang
5. Bin Chen
6. S. Joshua Swamidass

In Molecular Biology

1. Jiao Li
2. Zhiyong Lu
3. S. Joshua Swamidass
4. Atul J. Butte
5. Si Zheng
6. Bin Chen
7. Srinivasan Parthasarathy
8. Simon M. Lin

Citations in the corpus

• Graph embedding on biomedical networks: methods, applications and evaluations (2019, methodology): “For example, in DDAs prediction, matrix factorization-based techniques (Yang et al., 2014; Zhang et al., 2018d; Dai et al., 2015) are utilized to factorize the drug-disease association matrix and learn low-dimensional representations for drug/disease in the latent space.”
• A survey of current trends in computational drug repositioning (2016, methodology): “[62] used a causal inference-probabilistic matrix factorization approach to infer drug–disease associations.”