Linus Kohl linuskohl

## dataset.json
[
    {
        "identifier": "sg1153",
        "title": "The Rose Lover",
        "subjects": [
            "adult man",
            "walking, hiking (recreation)",
            "peeping, voyeur",
            "flowers: rose",
            "couple of lovers",

## umls_similarity_article_p3.py
# Create evaluation DataFrame containing BIOSSES pairings and additional information for evaluation
evaluation = pd.DataFrame(biosses_meta.loc[:,['Text1', 'Text2', 'Avg', 'Var']])
# Add CUI information
evaluation['Text1_CUIs'] = evaluation['Text1'].apply(lambda x: biosses_texts.loc[x,'UMLS_CUIs'])
evaluation['Text2_CUIs'] = evaluation['Text2'].apply(lambda x: biosses_texts.loc[x,'UMLS_CUIs'])
# Add UMLS terms
evaluation['Text1_UMLS_TERMS'] = evaluation['Text1'].apply(lambda x: biosses_texts.loc[x,'UMLS_Terms'])
evaluation['Text2_UMLS_TERMS'] = evaluation['Text2'].apply(lambda x: biosses_texts.loc[x,'UMLS_Terms'])
# Add texts for evaluation purposes
evaluation['Text1'] = evaluation['Text1'].apply(lambda x: biosses_texts.loc[x,'Text'])

## umls_similarity_article_p2.py
# Load similarities
cui_similarities = pd.read_csv("cui_pairings_out.csv", header=None, names=["cui_0","cui_1","lch","path","wup"])

# Build index for faster access
cui_similarities_reverse = cui_similarities.copy()
cui_similarities_reverse.rename(columns={"cui_0": "cui_1", "cui_1": "cui_0"}, inplace=True)
cui_table = pd.concat([cui_similarities, cui_similarities_reverse], sort=False)
cui_table.set_index(["cui_0","cui_1"], inplace=True)
cui_table = cui_table.sort_index(level='cui_1')
cui_table = cui_table.sort_index()

## umls_similarity_article_p1.py
import io
import os
import string
import csv
import xml
import re
import unicodedata
import itertools
import requests
from functools import partial

## biosses_texts.csv
Id,Text
0,It has recently been shown that Craf is essential for Kras G12D-induced NSCLC.
1,"The Bcl-2 inhibitor ABT-737 induces regression of solid tumors  and its derivatives are in the early clinical phase as cancer therapeutics; however, it targets Bcl-2, Bcl-XL, and Bcl-w, but not Mcl-1, which induces resistance against apoptotic cell death triggered by ABT-737."
2,Previous studies demonstrated that the decrease level of 5 hmC in tumors was due to the reduced expression of TET1/2/3 and IDH2 genes or tumor derived IDH1 and IDH2 mutations.
3,"More recently, IDH mutations and resultant 2-hydroxyglutarate (2HG) production in leukemia cells were reported to induce global DNA hypermethylation through impaired TET2 catalytic function."
4,Recent in vitro studies using shRNA-based approaches have suggested a role for TET2 in regulating myeloid differentiation and in regulating stem/progenitor cell proliferation.
5,"Recently, it was reported that expression of IDH1R132H suppresses TET2 activity and the mutations of

## biosses_meta.csv

          
            Id
            Text1
            Text2
            Annotator A
            Annotator B
            Annotator C
            Annotator D
            Annotator E
            Avg
            Var

            
              1
              0
              94
              4
              4
              4
              4
              4
              4
              0

            
              2
              1
              95
              3
              3
              3
              3
              3
              3
              0

            
              3
              2
              96
              2
              2
              3
              2
              2
              2.2
              0.2

            
              4
              3
              97
              3
              3
              4
              3
              3
              3.2
              0.2

            
              5
              4
              98
              3
              3
              4
              3
              3
              3.2
              0.2

            
              6
              5
              99
              3
              3
              4
              3
              3
              3.2
              0.2

            
              7
              6
              100
              1
              1
              3
              2
              1
              1.6
              0.8

            
              8
              7
              101
              3
              3
              3
              3
              3
              3
              0

            
              9
              8
              102
              2
              1
              1
              2
              1
              1.4
              0.3

## GetCUISimilarity.pl
#!/usr/bin/perl
#
# Author:         Linus Kohl
# E-Mail:         kohl@munichresearch.com
# Org:            MunichResearch
#
use strict;
use UMLS::Interface;
use UMLS::Similarity::lch;
use UMLS::Similarity::path;

## extract_links.js
// load jQuery if it is not loaded already
var jq = document.createElement('script');
jq.src = "https://cdnjs.cloudflare.com/ajax/libs/jquery/3.3.1/jquery.min.js";
document.getElementsByTagName('head')[0].appendChild(jq);
jQuery.noConflict();

clear();
var links = [];
$(CLASS).each(function(){ links.push($(this).attr('href')) });
links;

## jwt_helpers.py
from functools import wraps
from flask import request, abort, g
import json
import jwt
import requests
from typing import Union, List
from ..config import cache
from ..env import JWT_ISSUER, JWT_CLIENTID, JWT_AUDIENCE

DISCOVERY_URL = "/.well-known/oauth-authorization-server"

## hsozkult.py
#!/usr/bin/env python
# coding: utf-8
import os
import feedparser
import requests
import sqlite3
from sqlite3 import IntegrityError
from lxml import html
import lxml
from string import Template
	[
	{
	"identifier": "sg1153",
	"title": "The Rose Lover",
	"subjects": [
	"adult man",
	"walking, hiking (recreation)",
	"peeping, voyeur",
	"flowers: rose",
	"couple of lovers",
	# Create evaluation DataFrame containing BIOSSES pairings and additional information for evaluation
	evaluation = pd.DataFrame(biosses_meta.loc[:,['Text1', 'Text2', 'Avg', 'Var']])
	# Add CUI information
	evaluation['Text1_CUIs'] = evaluation['Text1'].apply(lambda x: biosses_texts.loc[x,'UMLS_CUIs'])
	evaluation['Text2_CUIs'] = evaluation['Text2'].apply(lambda x: biosses_texts.loc[x,'UMLS_CUIs'])
	# Add UMLS terms
	evaluation['Text1_UMLS_TERMS'] = evaluation['Text1'].apply(lambda x: biosses_texts.loc[x,'UMLS_Terms'])
	evaluation['Text2_UMLS_TERMS'] = evaluation['Text2'].apply(lambda x: biosses_texts.loc[x,'UMLS_Terms'])
	# Add texts for evaluation purposes
	evaluation['Text1'] = evaluation['Text1'].apply(lambda x: biosses_texts.loc[x,'Text'])
	# Load similarities
	cui_similarities = pd.read_csv("cui_pairings_out.csv", header=None, names=["cui_0","cui_1","lch","path","wup"])

	# Build index for faster access
	cui_similarities_reverse = cui_similarities.copy()
	cui_similarities_reverse.rename(columns={"cui_0": "cui_1", "cui_1": "cui_0"}, inplace=True)
	cui_table = pd.concat([cui_similarities, cui_similarities_reverse], sort=False)
	cui_table.set_index(["cui_0","cui_1"], inplace=True)
	cui_table = cui_table.sort_index(level='cui_1')
	cui_table = cui_table.sort_index()
	import io
	import os
	import string
	import csv
	import xml
	import re
	import unicodedata
	import itertools
	import requests
	from functools import partial
	Id,Text
	0,It has recently been shown that Craf is essential for Kras G12D-induced NSCLC.
	1,"The Bcl-2 inhibitor ABT-737 induces regression of solid tumors and its derivatives are in the early clinical phase as cancer therapeutics; however, it targets Bcl-2, Bcl-XL, and Bcl-w, but not Mcl-1, which induces resistance against apoptotic cell death triggered by ABT-737."
	2,Previous studies demonstrated that the decrease level of 5 hmC in tumors was due to the reduced expression of TET1/2/3 and IDH2 genes or tumor derived IDH1 and IDH2 mutations.
	3,"More recently, IDH mutations and resultant 2-hydroxyglutarate (2HG) production in leukemia cells were reported to induce global DNA hypermethylation through impaired TET2 catalytic function."
	4,Recent in vitro studies using shRNA-based approaches have suggested a role for TET2 in regulating myeloid differentiation and in regulating stem/progenitor cell proliferation.
	5,"Recently, it was reported that expression of IDH1R132H suppresses TET2 activity and the mutations of
Id	Text1	Text2	Annotator A	Annotator B	Annotator C	Annotator D	Annotator E	Avg	Var
1	0	94	4	4	4	4	4	4	0
2	1	95	3	3	3	3	3	3	0
3	2	96	2	2	3	2	2	2.2	0.2
4	3	97	3	3	4	3	3	3.2	0.2
5	4	98	3	3	4	3	3	3.2	0.2
6	5	99	3	3	4	3	3	3.2	0.2
7	6	100	1	1	3	2	1	1.6	0.8
8	7	101	3	3	3	3	3	3	0
9	8	102	2	1	1	2	1	1.4	0.3
	#!/usr/bin/perl
	#
	# Author: Linus Kohl
	# E-Mail: kohl@munichresearch.com
	# Org: MunichResearch
	#
	use strict;
	use UMLS::Interface;
	use UMLS::Similarity::lch;
	use UMLS::Similarity::path;
	// load jQuery if it is not loaded already
	var jq = document.createElement('script');
	jq.src = "https://cdnjs.cloudflare.com/ajax/libs/jquery/3.3.1/jquery.min.js";
	document.getElementsByTagName('head')[0].appendChild(jq);
	jQuery.noConflict();

	clear();
	var links = [];
	$(CLASS).each(function(){ links.push($(this).attr('href')) });
	links;
	from functools import wraps
	from flask import request, abort, g
	import json
	import jwt
	import requests
	from typing import Union, List
	from ..config import cache
	from ..env import JWT_ISSUER, JWT_CLIENTID, JWT_AUDIENCE

	DISCOVERY_URL = "/.well-known/oauth-authorization-server"
	#!/usr/bin/env python
	# coding: utf-8
	import os
	import feedparser
	import requests
	import sqlite3
	from sqlite3 import IntegrityError
	from lxml import html
	import lxml
	from string import Template