Camiel Mannens

@camielmannens.bsky.social

We are thrilled to share our new pre-print: “System-wide extraction of cis-regulatory rules from sequence-to-function models in human neural development”. S2F-deeplearning models can accurately encode enhancers, yet decoding these models into human-interpretable rules remains a major challenge.

A major KI initiative to recruit new assistant professors with outstanding proposals in all areas of medicine, biomedicine and public health. We offer an amazing research environment, great colleagues and generous startup packages. Check it out and get working on your applications! (repost please!)

Karolinska Institutet@ki.se · last yr.

Applications are now open! We are recruiting 20 Assistant Professors in a wide range of subject areas. We're looking for early-career researchers with strong scientific merits and future potential. 🔗 All positions: ki.se/en/about-ki/...

Check out our work on evaluating methods for predicting in vivo cell enhancer activity in the mouse cortex! Combined, scATAC peak specificity and sequence-based CREsted predictions gave the best predictive performance, aiming to advance genetic tool design for cell targeting in the brain.

Evaluating methods for the prediction of cell-type-specific enhancers in the mammalian cortex

Johansen et al. report the results of a community challenge to predict functional enhancers targeting specific brain cell types. By comparing multi-omics machine learning approaches using in vivo data...

cell.com

Very proud of two new preprints from the lab: 1) CREsted: to train sequence-to-function deep learning models on scATAC-seq atlases, and use them to decipher enhancer logic and design synthetic enhancers. This has been a wonderful lab-wide collaborative effort. www.biorxiv.org/content/10.1...

CREsted: modeling genomic and synthetic cell type-specific enhancers across tissues and species

Sequence-based deep learning models have become the state of the art for the analysis of the genomic regulatory code. Particularly for transcriptional enhancers, deep learning models excel at decipher...

biorxiv.org