justUseAnSvm

Can confirm: watched CNN live in my high school classroom up to and before the second plane hit. They thought it was a lost plane, or a navigational error. It was just your regular TV speculation. Watching the second plane hit was pretty fucked!

It bothers me to no end that we tortured KSM, and that he won't be brought to justice. In all the ways the US went astray, torture is digression from ourselves as "the shining city on the hill". Every western nation has decided not to torture for so many good reasons, it's just baffling that we'd ever consider doing it.

Even more concerning, is that 57% (can't find the citation...) of americans support torture under some circumstances: even though the intelligence is unreliable, that you cause PTSD in the torturer, and that it you'll never be able to try that person under a federal court.

Written under entrance of the U.S. Supreme Court is the saying "Equal Under The Law". We must back to that!

You're going to have to enjoy this career every step of the way instead of waiting for the ultimate end goal. I wouldn't recommend going into the career for the money

This is great advice! Thank you!

A couple things stand out to me, why I think this is fake: 1) everything is too clean. 2) there is tape at the end of the rifle. That’s idiotic for someone firing a rifle that heats up, not so much for air soft. 3) there’s no plate in that plate carrier. 4) your hands: way too clean 5) maybe the images aren’t recent, but right now it’s summer in Ukraine. That means the leaves are out. It’s just weird that the “proof” is old pics

That’s what I got, anyone else? Could be wrong, mods can we confirm?

Yea, I know some people working on this: causal network from data gathered in the ENCODE project. The basic problem, from my understanding, is that you are trying to model a vastly complex system (think lots and lots of hidden parameters) from not enough data. That's one challenge, the next challenge is conceptualize and then formalizing exactly what you are modeling, why this is biologically significant, an what improvement you are making over current understanding.

So in your example: if the goal is to predict gene expression, we would need to capture all of the variable needed to affect gene expression. That's a lot, and the way chromatin in set up, is different sets of things in different epigenetic regions. Further, you would not only need the measurable effect but the specifc regulator which is causing that effect. If you think about how the data comes in: 20k genes, 10k lncRNA, etc etc, you'll almost never get a sample that allows you to simple elucidate a relationship.

That said, I've done a couple quick checks between gene expression, as measured my mRNA, and ChIP-seq to prob if there were possibly regulatory effects. Finding statistically significant (FDR corrected p-value) results is generally hard, since there isn't a ton of data that is exactly comparable.

NN in biological just aren't at the level they could be at yet, and I think that's mainly due to the data problem. Its hard to get money for this type of exploratory research. If you are interested, I would suggest looking at the current models we can apply (like the paper you read), start understanding genomic data sources (ENCODE, GTEx, SRA, etc), then work up via more simplistic models, like Bayesian Networks. Check out Daphne Kohlers book on probabilistic graphical models to get a better idea of just how hard this stuff is to parameterize from a holistic view. I know I'm not an OP, but I used machine learning to study lncRNA in grad school, so I hope this helps. Best of luck, "some phd dropout"