Unraveling complex biology with biological knowledge graphs. Content licensed under CC BY 4.0.

Charles Tapley Hoyt

I’ve recently worked with clinical studies from ClinicalTrials.gov and other international registries.

Data Modeling and Integration with Clinical Trials

Here’s the books I read in 2024. If I were Dudley Dursley, I’d be very upset that I read one fewer new book than in 2023. But then, I’d remember that I re-read a lot of Cosmere in 2024 to prepare for

 Wind and Truth

, which was great.

Books I Read in 2024

I was working on making data about scholarly conferences more FAIR and a big question crossed my mind: what are all the conference venues? This post is about some queries I wrote for Wikidata, data issues I found, and a few drive-by curations that I did while looking for an answer, and my ideas for the future.

Exploring Event Venues in Wikidata

Brandon Sanderson has been releasing a few chapters a week of his upcoming novel, Wind and Truth, on his publisher’s website leading up to its December 6

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release. This includes the audiobook chapters, but they’re posted to Soundcloud and there’s no good way to listen at 1.6x speed. This post is a note sheet on how to download audio from Soundcloud and prepare it for my audiobook reader.

Downloading Audio from Soundcloud

PEP 735 introduced dependency groups in packaging metadata, which are complementary to optional dependencies in that they might not correspond to features in the package, but rather be something like development or release dependencies. I am slowly working towards updating my cookiecutter template cookiecutter-snekpack to use PEP 735. So far, uv and tox have released support - all that’s left is ReadTheDocs.

Dependency Groups and ReadTheDocs

The Open Researcher and Contributor Identifier (ORCID) database is an invaluable resource that supports the unambiguous identification of researchers. However, its first party data dump is too complex, verbose, and unstandardized for many use cases. This post describes open source software I wrote that automates downloading, processing, and exporting ORCID into a more usable form. I put the results on Zenodo under the CC0 license.

Easier ORCID

I’m working through making a contribution to pandoc that adds first-class support for author role annotations using the Contribution Role Taxonomy (CRediT) and also outputs compliant Journal Publishing Tag Set (JATS) XML. This has lead me down a (losing) journey with learning the Haskell programming language, so I thought I would post a short note on a function I tried to understand.

Some Haskell I Tried to Write

The International Society of Biocuration (ISB) partners with the journal Database to get discounts for its members when they publish there. This means the ISB’s executive committee needs to send a member list to the journal’s editor.

Programmatic Access to a Wordpress User List

This stub post contains my notes about funding for open source software. It doesn’t follow a story like a lot of my posts, and is more like an ever-evolving notes sheet.

Notes on Open Source Funding

Using Pydantic for encoding data models and FastAPI for implementing APIs on top of them has become a staple for many Python programmers.

Semantic Pydantic

I finally got back into reading! Over winter break 2022, I started the Stormlight Archive then followed up in 2023 by reading the entirety of Brandon Sanderson’s

 Cosmere

, as well as a some other fantasy, science fiction, and literary fiction. Here’s the list.

Books I Read in 2023

The Unified Medical Language System (UMLS) is a widely used biomedical and clinical vocabulary maintained by the United States National Library of Medicine. However, it is notoriously difficult to access and work with due to licensing restrictions and its complex download system. In the same vein as my previous posts about DrugBank and ChEMBL, this post describes open source software I’ve developed for downloading and working with this data.

Unlocking UMLS

This blog is normally about

 very serious science

, but I’m taking a break from that for the evening to advertize my band’s upcoming show on April 8

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in the SPH Music Masters Finale (aka, the German Battle of the Bands). We need your support!

The Representatives of Monkey Jack - German Battle of the Bands Finale

ChEMBL periodically curates clinical trial information into its DRUG_INDICATION table. However, there’s some weird inconsistencies in the way it references disease concepts in external vocabularies. This blog post is an exploration of that table.

The EFO_ID column in ChEMBL’s drug indications table isn’t what you think it is

Archival Resource Keys (ARKs) are flavor of persistent identifiers like DOIs, URNs, and Handles that have the benefit of being free, flexible with what metadata gets attached, and natively able to resolve to web pages. Name-to-Thing (N2T) implements a resolver for a variety of ARKs, so this blog post is about how that resolver can be re-implemented with the curies Python package. In a lot of ways, ARKs look and act like CURIEs.

Re-implementing the N2T ARK Resolver

I’ve just returned from the 17

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Annual International Biocuration Conference at the Indian Biological Data Centre (IBDC) in Faridabad, India.

Discussions and Follow-ups from Biocuration 2024

Graphviz is software for graph visualization written in C. PyGraphviz provides a nice Python wrapper for it. The issue is that getting Python to know about the C headers changes every few months. I’ll try and keep this blog post updated every time there are some changes.

Building Graphviz when installing PyGraphviz

Several controlled vocabularies and ontologies that aren’t themselves OBO Foundry ontologies use unsanctioned OBO PURLs. This post is about how to use the Bioregistry to identify which resources are doing this and to give some insight into how we arrived in this situation. Background on the OBO Foundry   The OBO Foundry is a set of independent, interoperable biomedical ontologies that aspire to using shared development principles.

Resources masquerading as OBO Foundry ontologies

Today’s short post is about three SPARQL queries I wrote to get bibliometric information about journals and publishers out of Wikidata. Each of the following queries can be readily copy-pasted into the Wikidata Query Service and run in the browser.

Querying Journals and Publishers in Wikidata

I’ve been working on applying strict static typing to my Python package class-resolver and ran into an interesting way of using generics in combination with parameter specification variables (i.e., ParamSpecs).  Normally, if you want to type annotate a function, you use the Callable, which works like the following: from collections.abc import Callable #: the [int] represents a function that takes in a single integer, #:  and returns a single

Using ParamSpec with Python Generics

This post describes an issue I’ve had with writing correct types when using PEP-696 defaults in typing.TypeVar. I posted the exploration in a companion repository on GitHub. The motivation behind this comes from my work in biomedical data integration and the semantic web.

A dilemma with PEP-696 default generics when using optional static typing in Python

Following the theme of my previous two posts, I’ve run into another typing conundrum where I want to unpack a pre-existing Callable into a class with Generic[P, T] where P is a parameter specification type (i.e. ParamsSpec)  After figuring out the right way to declare a generic featuring a ParamSpec, I updated the class-resolver package to use the shiny new (and more accurate) annotations.

I wish I could unpack Callables in Python type annotations

The Ontology Lookup Service (OLS) is now indexing dense embeddings for ontology terms constructed from term labels, synonyms, and descriptions using LLMs. I maintain a Python client library for the OLS (ols-client) and was recently asked to implement a wrapper to the OLS’s API endpoint that exposes these embeddings.

Text-based embeddings of ontology terms

Assembling and inferring missing semantic mappings is a timely problem in biomedical data and knowledge integration. I’ve been developing the Semantic Mapping Assembler and Reasoner (SeMRA) as a generic toolkit for this. In this blog post, I highlight its inference capabilities. SeMRA implements the chaining and inference rules described in the SSSOM specification.

Inference over Semantic Mappings with SeMRA

I was recently nominated for the International Society for Biocuration’s Excellence in Biocuration Early Career Award (results will be announced on June 14

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!). This made me curious about how to model nominations and awards on Wikidata. In this post, I’ll describe how to curate awards, nominations, recipients, and how to make SPARQL queries to get them.

Modeling and Querying Awards in Wikidata

I’ve been working on improving reproducibility in the field of cheminformatics for some time now. For example, I’ve written posts about making data from DrugBank and ChEMBL more actionable. Over the last year, I’ve been preparing a concept with the editors of the Journal of Cheminformatics on how to include an assessment of reproducibility to reviews of manuscripts submitted to the journal.

Reproducibility Pilot in the Journal of Cheminformatics

The Bioregistry is a community-driven registry of semantic spaces and their metadata. When I learned about BiomarkerKB at the International Society for Biocuration’s 18th Annual International Biocuration Conference, I was excited to curate new records (and prefixes) in the Bioregistry to cover BiomarkerKB’s semantic spaces on biomarkers.

The Bioregistry and BiomarkerKB

The Bioregistry is a database and toolchain for standardization of prefixes, CURIEs, and URIs that appear in linked (open) data. While I created it in 2019 as a component of PyOBO in order to support parsing database cross-references appearing in biomedical ontologies, it has since become an independent project with a community-driven governance model and much broader applications. This post is a first attempt to quantify its usage and impact.

Measuring the impact of the Bioregistry

I’ve recently submitted an article to the Journal of Open Source Software (JOSS) describing chembl-downloader, a Python package for automating downloading and using ChEMBL data in a reproducible way. In this post, I use chembl-downloader to show how the number of compounds, assays, activities, and other entities in ChEMBL have changed over time.

I’ve recently submitted an article to the Journal of Open Source Software (JOSS) describing chembl-downloader, a Python package for automating downloading and using ChEMBL data in a reproducible way. In this post, I use chembl-downloader to show how the number of compounds, assays, activities, and other entities in ChEMBL have changed over time. ChEMBL has made 37 releases so far.

A historical analysis of ChEMBL

Using standard CURIE prefixes and URI prefixes in semantic web artifacts such as Resource Description Framework (RDF) promotes interoperability, enables reuse in downstream data integration, and makes data more FAIR. The Bioregistry defines a set of standard CURIE prefixes and URI prefixes against which RDF files can be validated/standardized.

Biopragmatics