This repository contains Python code, written by Daniel Himmelstein from 2012-2015 during his PhD in the Baranzini Lab at UCSF. This includes code that contributed to the following studies:
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Heterogeneous Network Edge Prediction: A Data Integration Approach to Prioritize Disease-Associated Genes
Daniel S. Himmelstein, Sergio E. Baranzini
PLOS Computational Biology (2015-07-09) https://doi.org/98q
DOI: 10.1371/journal.pcbi.1004259 · PMID: 26158728 · PMCID: PMC4497619 -
iCTNet2: integrating heterogeneous biological interactions to understand complex traits
Lili Wang, Daniel S. Himmelstein, Adam Santaniello, Mousavi Parvin, Sergio E. Baranzini
F1000Research (2015-09-28) https://doi.org/ghfv3v
DOI: 10.12688/f1000research.6836.2 · PMID: 26834985 · PMCID: PMC4706053 -
The hetnet awakens: understanding complex diseases through data integration and open science [Thesis]
Daniel Himmelstein
figshare (2017) https://doi.org/b2nz
DOI: 10.6084/m9.figshare.4724797 · ISBN: 9781339919881
Code related to Project Rephetio is released separately, and largely post-dates this repository.
The code to create the PLOS Comp Bio (2015) network, i.e. het.io-dag, is in projects/gene_disease_hetnet/createnet.py (access data here).
This repository originally used mercurial and was hosted on BitBucket at https://bitbucket.org/dhimmel/serg.
Unfortunately, BitBucket deleted mercurial repos in 2020 without an automated migration path.
On 2020-10-15, dhimmel realized this source code was no longer online.
The repo could not be retrieved from BitBucket, but dhimmel had a local copy in ~/Documents/serg/pycode.
The local copy matched the version archived by Software Heritage.
Using the hg-fast-export utility, dhimmel exported the repository to git and uploaded it to https://github.com/dhimmel/serg-pycode.
This code is uploaded to GitHub for archiving and historical purposes. The extent this python code was used to perform certain studies and analyses requires a bit of investigation. The repository is not actively maintained nor accepting feature requests. However, questions about existing code or analyses are welcome via GitHub Issues. Furthermore, any documentation contributions are appreciated.