If you are looking in terms of software-based solutions for an issue like this, the best resource is likely via Stanford University's SNAP group, which publishes BioSNAP datasets [1], which can be used for scaling.
For example, in the US, a lot of people are on a bunch of prescription drugs. This is called polypharmacy. Using AI, the SNAP group created AI to identify side-effects when on several drugs [2]. There is excellent sample code for this via the link I provided, that can be viewed on GitHub. Generally this is the case for all of the BioSNAP repositories.
There are also AI tools which Stanford has created which can help augment a deep search through the literature for a rare disease. For example, this "Disease-function association network" [3] can give useful outputs to help one direct a search for finding a certain rare disease.
> "This is a disease-function association network that contains information on relationships between diseases and cellular functions. Cellular functions capture biological processes (e.g., pathways made up of the activities of multiple proteins such as cell communication), cellular components (e.g., components where gene products are active such as mitochondria), and molecular functions (e.g., molecular activities of gene products such as drug binding). Nodes represent diseases and functions, and edges indicate associations between them."
The problem with AI is that it is intellectually bankrupt: it will tell you what it thinks, but it will not tell you why. So, it is critical to develop excellent intuition as an individual.
disabled|4 years ago
I also hope this helps.
If you are looking in terms of software-based solutions for an issue like this, the best resource is likely via Stanford University's SNAP group, which publishes BioSNAP datasets [1], which can be used for scaling.
For example, in the US, a lot of people are on a bunch of prescription drugs. This is called polypharmacy. Using AI, the SNAP group created AI to identify side-effects when on several drugs [2]. There is excellent sample code for this via the link I provided, that can be viewed on GitHub. Generally this is the case for all of the BioSNAP repositories.
There are also AI tools which Stanford has created which can help augment a deep search through the literature for a rare disease. For example, this "Disease-function association network" [3] can give useful outputs to help one direct a search for finding a certain rare disease.
> "This is a disease-function association network that contains information on relationships between diseases and cellular functions. Cellular functions capture biological processes (e.g., pathways made up of the activities of multiple proteins such as cell communication), cellular components (e.g., components where gene products are active such as mitochondria), and molecular functions (e.g., molecular activities of gene products such as drug binding). Nodes represent diseases and functions, and edges indicate associations between them."
The problem with AI is that it is intellectually bankrupt: it will tell you what it thinks, but it will not tell you why. So, it is critical to develop excellent intuition as an individual.
[1] Stanford BioSNAP Repositories: http://snap.stanford.edu/biodata/index.html
[2] Polypharmacy side-effect association network: http://snap.stanford.edu/biodata/datasets/10017/10017-ChChSe...
[3] Disease-function association network: http://snap.stanford.edu/biodata/datasets/10019/10019-DF-Min...