futianfan / clinical-trial-outcome-prediction Goto Github PK

import data2graph

Thanks for the great work! Unfortunately the Dropbox link in #7 seems no longer valid? If so, would you mind re-uploading it? Many thx!

Hi,

thank you for your precious work.

I am trying to reproduce tutorial_HINT.ipynb but I found the following error

`---------------------------------------------------------------------------
UnicodeDecodeError Traceback (most recent call last)
in
----> 1 train_loader = csv_three_feature_2_dataloader(train_file, shuffle=True, batch_size=32)
2 valid_loader = csv_three_feature_2_dataloader(valid_file, shuffle=False, batch_size=32)
3 test_loader = csv_three_feature_2_dataloader(test_file, shuffle=False, batch_size=32)

~\Desktop\CLINICALTRIAL\HINT\dataloader.py in csv_three_feature_2_dataloader(csvfile, shuffle, batch_size)
103 def csv_three_feature_2_dataloader(csvfile, shuffle, batch_size):
104 with open(csvfile, 'r') as csvfile:
--> 105 rows = list(csv.reader(csvfile, delimiter=','))[1:]
106 ## nctid,status,why_stop,label,phase,diseases,icdcodes,drugs,smiless,criteria
107 nctid_lst = [row[0] for row in rows]

~\Miniconda3\envs\predict_drug_clinical_trial\lib\encodings\cp1252.py in decode(self, input, final)
21 class IncrementalDecoder(codecs.IncrementalDecoder):
22 def decode(self, input, final=False):
---> 23 return codecs.charmap_decode(input,self.errors,decoding_table)[0]
24
25 class StreamWriter(Codec,codecs.StreamWriter):

UnicodeDecodeError: 'charmap' codec can't decode byte 0x90 in position 1027: character maps to `

Do you know how could I solve it?

Regards,
Federico

when I am running the command: conda env create -f conda.yml, it returns

Collecting package metadata (repodata.json): done
Solving environment: failed

ResolvePackageNotFound:
  - ninja==1.9.0=py37h04f5b5a_0
  - readline==8.0=h1de35cc_0
  - zlib==1.2.11=h1de35cc_3
  - gettext==0.19.8.1=hb0f4f8b_2
  - tk==8.6.10=hb0a8c7a_0
  - freetype==2.10.2=ha233b18_0
  - python==3.7.7=hf48f09d_4
  - libxslt==1.1.34=h83b36ba_0
  - lz4-c==1.9.2=h0a44026_0
  - llvm-openmp==10.0.0=h28b9765_0
  - cffi==1.14.0=py37hc512035_1
  - openssl==1.1.1h=haf1e3a3_0
  - py-boost==1.67.0=py37h6440ff4_4
  - ...

Hey! I was wondering what's the best way to replicate the results of this project and predict clinical trial outcomes? Is there any specific file or code I should start with?

Great Work! Just one question: how can we get the full information for the drugbank data?
I noticed that it can be downloaded from drugbank website, https://go.drugbank.com/releases/latest, but I am not sure how to pre-process it after we obtain the full database.xml file. Thanks!

Hi, Thanks again for curating this dataset! I was just wondering where I can find the complete version of drugbank_mini.csv, since using drug names as query through drugbank's vocabulary file does not always output a valid drugbank id. drugbank_mini.csv contains more information and might be useful to map those ambiguous ones to correct ids.

When I run conda env create -f conda.yml, it returns:

Pip subprocess error:
ERROR: Could not find a version that satisfies the requirement drugdataloader==0.1 (from -r /Users/zzachw/Projects/code_review/clinical-trial-outcome-prediction/condaenv.rzi2syk0.requirements.txt (line 10)) (from versions: none)
ERROR: No matching distribution found for drugdataloader==0.1 (from -r /Users/zzachw/Projects/code_review/clinical-trial-outcome-prediction/condaenv.rzi2syk0.requirements.txt (line 10))

When I run src/learn_phaseI.py

File "/home/user/reproduce/clinical-trial-outcome-prediction/src/utils.py", line 12, in plot_hist
import seaborn as sns
ModuleNotFoundError: No module named 'seaborn'

Hi, Thanks for open sourcing this. I have a few questions regarding the paper:

1. How the outcome label is determined? It seems the outcome label comes from IQVIA so the process of generating it was not shown in this repo. In the paper, it was described as The trial is labelled as success if p-value is less than 0.05 and negative if p-value is higher than 0.05. I am not sure if this is also applicable for Phase I trials. Could you elaborate more on that?
2. How does ADMET pretraining help with Phase II prediction? Since majority of Phase II trials failed due to efficacy, I am not sure why pretraining ADMET could help with the predictions. Instead, understanding the disease-target relation may help.

from graph_visualize_interpret import data2graph

Thanks in advance!