elipapa

# Sensible Bash - An attempt at saner Bash defaults
# Repository: https://github.com/mrzool/bash-sensible

## GENERAL OPTIONS ##
set -o noclobber
shopt -s checkwinsize
PROMPT_DIRTRIM=2
bind Space:magic-space
shopt -s globstar 2> /dev/null
shopt -s nocaseglob;

elipapa

apiVersion: extensions/v1beta1
kind: Deployment
metadata:
  name: apiproxy-eu
spec:
  replicas: 1
  template:
    metadata:
      labels:
        name: apiproxy-eu

elipapa

apiVersion: v1
kind: Service
metadata:
  name: apiproxysvc-node
  labels:
    name: apiproxysvc-node
spec:
  type: NodePort
  ports:
  - name: http

elipapa

''' 
standalone script to create a genes JSON object.
'''

#!/usr/bin/env python3
import os
import time
import pandas as pd
from sqlalchemy import create_engine

elipapa

# pip install ontoma

from ontoma.interface import OnToma
ot = OnToma()

# the following will download the latest EFO OBO file from github
# and output a tab separated files of labels <=> codes to a file
for k,v in ot.efo_to_name.items():
  print(k,v,file=open('efolabel.txt','a'),sep="\t")

elipapa

Recsys resources

Xavier Amatriaian created some of the best basic tutorials:

the biggest one was from a recsys summer school: https://www.slideshare.net/xamat/recommender-systems-machine-learning-summer-school-2014-cmu (2 hr video here: 2hr video )

He has been prolific afterwards:

the-recommender-problem-revisited

elipapa

{
  "basics": {
    "name": "Eliseo Papa",
    "label": "Director AI Engineering at AstraZeneca",
    "picture": "",
    "email": "[email protected]",
    "phone": "",
    "website": "",
    "summary": "I trained as a biomedical engineer and then as a clinician. Now I use my background to connect the dots between medicine and engineering. I work on building AI and data science product that can speed up the drug development process and bring medicines in the hands of patients faster.",
    "location": {