Nezar Abdennur

@nvictus.bsky.social

computational biologist / biological computer / asst prof @UMassChan / phd @MIT / http://abdenlab.org

High-dimensional data is hard to understand. But is it truly cursed? To help you build better intuition for high-dimensional datasets, @lekschas.de and I developed dtour, a visualization tool for smoothly navigating through data projections.

Many of you may have received pessimism from me over the years about using Hi-C to predict distal regulatory effects, mired with paradoxical optimism that cohesin loop extrusion is key. Well, the theoretical story underlying my seemingly paradoxical rambling is finally ready to be told. 👇

Elphege Nora Lab at UCSF@elphegenoralab.bsky.social · 3mo ago

Why can't we explain enhancer action despite 2 decades of chromosome conformation technologies? 😬 Our new study spearheaded by Leonid Mirny's group points to a flaw in our assumptions, and to a solution from physical principles By @timothyfoldes.bsky.social 💻& @karissalhansen.bsky.social 🧪 🧵👇

In the genomics community, we have focused pretty heavily on achieving state-of-the-art predictive performance. While undoubtedly important, how we *use* these models after training is potentially even more important. tangermeme v1.0.0 is out now. Hope you find it useful!

(4) bpnet-lite: Load official Chrom/BPNet models into PyTorch for downstream tangermeme integration. Improved command-line tools + docs. Still concerns about perf of models trained from scratch -- will be resolved next version! github.com/jmschrei/bpn... bsky.app/profile/jmsc...

GitHub - jmschrei/bpnet-lite: This repository hosts a minimal version of a Python API for BPNet.

This repository hosts a minimal version of a Python API for BPNet. - jmschrei/bpnet-lite

github.com

Jacob Schreiber@jmschreiber91.bsky.social · last yr.

Last week I released bpnet-lite v0.5.0. BPNet/ChromBPNet are powerful models for understanding regulatory genomics from @anshulkundaje.bsky.social's group, and now it's way easier to go from raw data to trained models and analysis + results in PyTorch Try it out with `pip install bpnet-lite`