neo4j_notes.py

"""
Link to reference card for cypher:
https://neo4j.com/docs/cypher-refcard/current/
Cypher style guide:
https://neo4j.com/developer/cypher/style-guide/
Neo4j het guide:
http://neo4j.het.io/guides/hetionet.html
Python language reference:
https://docs.python.org/3/reference/index.html
"""

"""
#10.20.20 
"""
"""Tables:"""
MATCH path=(g:Gene)--(d:Disease) RETURN g.name,count(d.name) LIMIT 100
#lists 100 genes and number of relationships with diseases in table form
MATCH path=(g:Gene)--(d:Disease) RETURN DISTINCT g.name,count(DISTINCT d.name),collect(DISTINCT d.name) LIMIT 100
#lists 100 genes, number of relationships with diseases, and disease names in table form, similar to above
MATCH (d:Disease)--(g:Gene) RETURN DISTINCT d.name,count(DISTINCT g.name),collect(DISTINCT g.name) LIMIT 100
#lists 100 diseases, number of related genes, and lists disease names in table form, opposite relationship b/w gene and disease compared to above
MATCH path=(p:Pathway)--(g:Gene)--(d:Disease) RETURN p.name, count(g.name), count(d.name) LIMIT 100
#lists 100 pathways and number of related genes and diseases in table form; number of genes=number of diseases
"""Graphs:"""
CALL db.schema()
#Shows the data model metagraph for Hetionet
MATCH path=(g:Gene)--(d:Disease) RETURN path LIMIT 100
#Shows the relationships b/w 100 genes and 2 diseases in a graph

"""
11.25.20
"""
#from Lesson 1 from NLP folder:
from tensorflow.keras.preprocessing.text import Tokenizer
#imports the tokenizer functionality

sentences = [
    'i love my dog',
    'I, love my cat',
    'You love my dog!'
]

tokenizer = Tokenizer(num_words = 100)
#establishes that there are 100 words
tokenizer.fit_on_texts(sentences)
word_index = tokenizer.word_index
#updates internal vocabulary based on a list of texts. Creates a word index based on the input.
print(word_index)