Joep de Ligt 🧬 PhD

@joepdel.bsky.social

Lead data @HartwigMedicalFoundation, Amsterdam, Netherlands. Honorary fellow @OtagoUniversity. Active in the field of Bioinformatics and Genetics with a focus on human diseases. Lover of nature and (book) nerd. he/him https://orcid.org/0000-0002-0348-419X

🧬🖥️Drug response prediction is a machine learning challenge with immense potential for precision medicine. Our latest preprint introduces DrEval, a comprehensive benchmarking framework to evaluate state-of-the-art methods, uncover widespread issues, and guide the development of more robust models.

Judith Bernett@judith-bernett.bsky.social · last yr.

🧬🖥️So excited to show you the outcome of @pascivers.bsky.social and my latest project: "From Hype to Health Check: Critical Evaluation of Drug Response Prediction Models with DrEval" doi.org/10.1101/2025.05.26.655288, published with M. Picciani, M. Wilhelm, K. Baum & @itisalist.bsky.social. 🧵1/10

Overview of the DrEval framework. Via input options, implemented state-of-the-art models can be compared against baselines of varying complexity. We address obstacles to progress in the field at each point in our pipeline: Our framework is available on PyPI and nf-core and we follow FAIReR standards for optimal reproducibility. DrEval is easily extendable as demonstrated here with a pseudocode implementation of a proteomics-based random forest. Custom viability data can be preprocessed with CurveCurator, leading to more consistent data and metrics. DrEval supports five widely used datasets with application-aware train/test splits that enable detecting weak generalization. Models are free to use provided or custom cell line– and drug features. The pipeline supports randomization-based ablation studies and performs robust hyperparameter tuning for all models. Evaluation is conducted using meaningful, bias-resistant metrics to avoid inflated results from artifacts such as Simpson’s paradox. All results are compiled into an interactive HTML report. Created in https://BioRender.com.

Please check out our new approach to modeling somatic mutation signatures. DAMUTA has independent Damage and Misrepair signatures whose activities are more interpretable and more predictive of DNA repair defects, than COSMIC SBS signatures 🧬🖥️🧪 www.biorxiv.org/content/10.1...

Damage and Misrepair Signatures: Compact Representations of Pan-cancer Mutational Processes

Mutational signatures of single-base substitutions (SBSs) characterize somatic mutation processes which contribute to cancer development and progression. However, current mutational signatures do not ...

biorxiv.org

📣 ELIXIR has released an updated international strategy to guide its engagement and #collaborations beyond Europe 🌍. Find out how 🙌 ELIXIR will work with international initiatives and partner regions worldwide to address #lifescience data challenges 👉 elixir-europe.org/news/interna...

ELIXIR releases new international strategy | ELIXIR

ELIXIR has released an updated international strategy to guide its engagement and collaborations beyond Europe. The revised framework outlines how ELIXIR will work with international initiatives and…

loom.ly

The review found consensus that cannabis is effective for treating symptoms of cancer and chemo like pain and nausea. Perhaps more surprisingly, it also found consensus that cannabis can fight cancerous cells. But a lot more research is needed to show that would work in human patients.

"the quality of input data is more important when improving forecasts than the complexity of the ML architecture" @fdg10371.bsky.social Although it may seem obvious, it deserves reiteration, and it applies to other fields, not only global fires www.nature.com/articles/s41... 🧪🌎🔥🌲🔥🌿 wildfire

Global data-driven prediction of fire activity - Nature Communications

The ability to predict wildfires-such as those that recently devastated Los Angeles and Canada-is advancing rapidly with the help of AI. This study shows that to improve accuracy and reliability, we m...

nature.com