what_are_you_saying

As someone currently writing a Ph.D. research proposal and constantly finding myself frustrated with conflicting results in publications with nearly identical experiments, I would love to see an AI capable of parsing through hundreds of research papers, being able to comprehend the experiments and methods outlined (likely the hardest part), then compiling all the results (both visual and text-based) into a database that shows where these experiments differ, which results are the most consistently agreed upon, and which discrepancies seem to best explain the differences in results.

I can't help but feel that once the database is created a simple machine learning algorithm would be able to identify which variables best predict which results and be able to find extremely compelling effects that a human may never notice. My biggest problem is trying to make connections between a paper I read 300 pages back (or even remember the paper for that matter) and the one I am reading now.

With the hundreds of thousands of papers relevant to any particular field it would be impossible for any researcher to actually read and retain even a small fraction of the relevant research in their field. Every day I think about all the data already out there ready to be mined and analyzed and the massive discoveries that have already been made, but not realized due, to the limitations of the human brain.

Are there any breakthroughs on the horizon for an AI that can comprehend written material with such depth and be able to organize it in a way that can be analyzed by simple predictive modeling?

Back when I was a LG/WSI we would spend downtime testing our breath holding and underwater swimming. My longest breath hold was 3.5min w/ hyperventilation (not recommended people, high risk of blackout, we had a system where we tapped a persons arm every few seconds to confirm consciousness). Longest underwater swim was 1.25 laps on a 25m pool (~62.5m). Those wall pushes really helped since a good one could get you halfway across without a stroke.

Was a fun way to burn downtime and it was cool to see how much more you're capable of than you think you are.

For Rockwell:

After looking over your paper on using CRISPR to identify gene expression regulation elements I had a question you may be able to answer.

I have been thinking for a while about using machine learning to analyze large amounts of RNAseq data modeled against a reference genome to discover complex biopathways of any given treatment. Would it be possible to simultaneously consider variable expression of TFs, known regulatory sequences found on the reference genome, iRNA, lncRNA, mRNA, etc expression and allow a NN to build a predictive model of expression changes to one another. This would then be able to trace back all changes within the transcriptome to a regulator not explained by changes found within the trancriptome to suggest possible primary targets or non-genomic influencers for any given treatment based purely off RNAseq data collected from said treatment?

I feel like a comprehensive model which uses mined data from all available RNAseq databases may be able to save researchers a ton of time by suggesting treatment biopathways based off of a simple pilot RNAseq study which would save a lot of resources and allow researchers to do less guess work and give them a library of specific predicted interactions to validate with functional assays. If done correctly, the NN could then take in the results of the validation studies to modify and improve its predictive model over time. Given enough data and validation it even seems like this would allow the NN to create an all-inclusive, highly-accurate model of gene expression effects for the organism used (likely humans) for any given treatment regardless of currently available training data for said treatment.

What are your thoughts on this? Do you think this could be done using current machine learning capabilities or am I overly ambitious and underestimating the complexity of such a model?

As an EMT who worked on ski patrol, at least half of our dispatches are from someone who decided they were done skiing so they sit down and wait for us to give them a ride down.

However, it's still better than having to get them after they push themselves too far and get hurt.

Come back to Bozeman!!