Cheng, F et al. A genome-wide positioning systems network algorithm for in silico drug repurposing. Nature Communications 10, (2019). https://www.nature.com/articles/s41467-019-10744-6
https://github.com/ChengF-Lab/GPSnet
- What kind of research(abstract)?
The author made a new algorithm, called GPSnet, for drug repurposing. The algorithm maps targeting disease modules, which are derived from DNA/RNA sequencing profiles, to the human protein-protein interaction network.
They used the algorithm for whole-exome sequencing and transcriptome profiles from < 5000 patients with 15 cancer types from TCGA. The algorithm succeeded in predicting drug responses and discovered 140 new drugs effect. In addition, they validated the effectiveness of the drugs to a specific disease.
- What is progress from previous research?
In their opinion, currently, there is massive progress in precision oncology or network-based precision medicines. However, it is unclear that how to use these methods for precision medicine. From this situation, they try to implement new methods and then show some results to describe the effectiveness of this kind of research for real problems. Their primary purpose is that they would like to show the feasibility of disease identification individually from the large-scale DNA sequencing and transcriptomic profiles.
- What is the specific topic?
Making the algorithm that can be used for drug repurposing technically and validated by real experiments.
- How did the method evaluate its usefulness?
By dry experiments.
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Are there any discussions?
-
What is the next article you should read?
Cheng, F et al. A genome-wide positioning systems network algorithm for in silico drug repurposing. Nature Communications 10, (2019). https://www.nature.com/articles/s41467-019-10744-6
https://github.com/ChengF-Lab/GPSnet
The author made a new algorithm, called GPSnet, for drug repurposing. The algorithm maps targeting disease modules, which are derived from DNA/RNA sequencing profiles, to the human protein-protein interaction network.
They used the algorithm for whole-exome sequencing and transcriptome profiles from < 5000 patients with 15 cancer types from TCGA. The algorithm succeeded in predicting drug responses and discovered 140 new drugs effect. In addition, they validated the effectiveness of the drugs to a specific disease.
In their opinion, currently, there is massive progress in precision oncology or network-based precision medicines. However, it is unclear that how to use these methods for precision medicine. From this situation, they try to implement new methods and then show some results to describe the effectiveness of this kind of research for real problems. Their primary purpose is that they would like to show the feasibility of disease identification individually from the large-scale DNA sequencing and transcriptomic profiles.
Making the algorithm that can be used for drug repurposing technically and validated by real experiments.
By dry experiments.
Are there any discussions?
What is the next article you should read?