I tried WAL, but like you said, it froze up during multiple writes (when creating records, not updating). I guess what I needed was a write queue - not sure if that exists.
It's great! However, it's only meant for local systems. Once you need to connect over a network or robustly handle simultaneous requests, you need something like postgres.
pypi put out a survey a while back that was full of bs questions about dei fluff. the lack of subject matter made me really question the competence of the project staff.
Yes! Why is every company hiring for LLM talent? Companies that have no business doing so. They probably don't even know that supervised machine learning exists.
The functional predictions related to "non-coding" variants are big here. Non-coding regions, referred to as the dark genome, produce regulatory non-coding RNA's that determine the level of gene expression in a given cell type. There are more regulatory RNA's than there are genes. Something like 75% of expression by volume is ncRNA.
You're right, DNA damage is just one of the types of genetic variation in cancer. There are many other structural variations that act like remixes.
"Maybe we need to start culturing and DNA testing cancers."
I assure you this is being done at a massive scale.
Due to cellular stress, cancer cells disobey multi-cellular governance. They behave more like independent organisms fighting for survival, reverting to primal programming.
Haven't tried it. S3 Tables sounds like a great idea. However, I am wary. For it to be useful, a suite of AWS services probably needs to integrate with it. These services are all managed by different teams that don't always work well together out of the box and often compete with redundant products. For example, configuring SageMaker Studio to use an EMR cluster for Spark was a multi-day hassle with a lot of custom (insecure?) configuration. How is this different from other existing table offerings? AWS is a mess.