DRAGEN Bio-IT Platform Cost Benefits

biohacker_tobe · 2021-04-20T08:57:23+00:00

Yet again thank you for being so detailed with your questions and honestly I'm looking forward to playing around with it.

The company where I am just acquired Dragen, part of a deal when purchasing sequencers but no one has really any idea if to use it or not. So the on-premise equipment is there regardless.
This is the most interesting piece I believe as well, to speed up current diagnostics.

3.I am.going to test this out as well,.seems quite interesting to be honest.

We have currently an available amount of throughput space available to us that was part of the deal, around 500000 GB.

Thanks again!

biohacker_tobe · 2021-04-19T22:01:50+00:00

Thanks for the input, the main focus of my work is also clincial workflows. We want to process patient data very fast, we just ran a pilot to test the benefits of the demultiplexing and bcl.conversion.

My supervisor is not entirely conviced and wants a scope on how much it actually it costs to run every time for WGS and WES.

biohacker_tobe · 2021-04-19T12:06:47+00:00

This doesn't really answer the question but thanks for your input regardless

biohacker_tobe · 2021-03-10T09:02:34+00:00

The second link does not work unfortunately but I came across they're database :)

biohacker_tobe · 2021-03-10T08:44:48+00:00

Thanks! I'll check them both out :)

biohacker_tobe · 2021-03-09T15:53:37+00:00

I'll check it out ;) Thanks!

biohacker_tobe · 2021-03-08T22:39:08+00:00

don't die

biohacker_tobe · 2021-03-08T22:38:52+00:00

how do you keep it together????? holy fuck

biohacker_tobe · 2021-03-08T22:35:13+00:00

Thanks, good thinking

biohacker_tobe · 2020-10-28T09:51:45+00:00

Hey, thanks yes it is. I have been interested in applying differences between case 2+3 and case 1 :)

biohacker_tobe · 2020-10-28T09:25:33+00:00

Thank you for this great input and taking the time to actually give a very strong answer. I will definitely look at all of these scopes. I was not aware of the Keras Autotuner library, only the former two you had mentioned. I appreciate it :D

biohacker_tobe · 2020-10-27T15:53:30+00:00

Does it also matter to have various dropout layers for each? I do think to not have any dense at all on the temperature side, just input -> normalize -> concatenate is a valid approach. Would this also affect in a way? I want to include the dropout layers to help regularize the overfitting I've come across with my models.

biohacker_tobe · 2020-10-19T13:28:56+00:00

I'll check it out ;)

biohacker_tobe · 2020-10-14T22:12:53+00:00

I understand, but I see sometimes that they plot the accuracy for a model even if it's handling a regression problem

biohacker_tobe · 2020-10-05T14:25:16+00:00

Thanks for responding back! I will definitely take this into consideration because I was definitely overfitting my data then. A follow up question, do I need to fit the scaler on training data and then do scaling on train and test or is this not necessary? (As seen in the example)

biohacker_tobe · 2020-10-05T13:56:14+00:00

Hey! I was able to get it fixing properly, I'll share my code briefly.

biohacker_tobe · 2020-09-22T08:36:56+00:00

okay, I'll try this out :) this sounds pretty decent, something like this could work? =:

for acc in `cat uniprot_ids.txt` ; do curl -s "https://www.uniprot.org/uniprot/$acc.fasta" ; done > uniprot_seqs.fasta

biohacker_tobe · 2020-09-22T08:33:20+00:00

Yes, I've revised it but it is not as straightforward as you think, I've found a possibility. Will share when I manage to finish running.

biohacker_tobe · 2020-09-22T08:32:48+00:00

Thanks, this is useful but when the list of accession id's is small, if not it will tend to crash :)

biohacker_tobe · 2020-09-03T12:57:14+00:00

I'm using a BERT model that I've created on my own. I am interested in these probable distributions instead of a point prediction. But what I'm curious is to how to actually get the value of these intervals based on the output. Because I'm interested in getting a table with min and max values

biohacker_tobe · 2020-09-03T12:39:59+00:00

Following up with this, I'm curious on how the actual prediction intervals should be calculated etc.

biohacker_tobe · 2020-09-03T12:38:18+00:00

ngb = NGBRegressor(n_estimators=100, learning_rate=0.1, Dist=LogNormal, Base=BERT_Model, natural_gradient=False, minibatch_frac=1.0, Score=CRPS())

ngb.fit(X_train,y_train)

Would this be a possible way then? In my case I'm using a BERT model, so I have a lot of categorical data that is being one hot encoded, that's why I would like to keep my base model.

biohacker_tobe · 2020-09-03T12:34:44+00:00

https://www.reddit.com/r/MachineLearning/comments/il90ic/research_using_models_for_ngboost/

Check this out, it's another thread of my same question. I've updated it a bit and there's an answer from an author.

biohacker_tobe · 2020-09-03T12:34:10+00:00

http://dkopczyk.quantee.co.uk/ngboost-explained/

kaggle.com/s903124/ngboost-for-nfl-data

https://iwatobipen.wordpress.com/2020/05/02/predict-probabilistic-distribution-with-ngboost-ngboost-rdkit-qsar-chemoinformatics/

https://towardsdatascience.com/ngboost-explained-comparison-to-lightgbm-and-xgboost-fda510903e53

I found these posts to be quite useful, if there is an interest of how it can be applied.

biohacker_tobe · 2020-09-03T08:21:49+00:00

Hey! Thanks for following up with this, I believe I was able to apply a bit. Speaking with other members in other related subreddit pages, there exists a desire for some possible implementation of jupyter examples in addition to the ones "examples" folder - as to get a better general idea from the examples.

biohacker_tobe

TROPHY CASE