So excited that our work on predicting gene expression from histone modifications using deep learning is out in Nucleic Acids Res today. Brilliant to work with lead author Alan Murphy and collaborators Aydan Askarova, Boris Lenhard and Nathan Skene 🧬⭐️🙏 academic.oup.com/nar/advance-ar…

Predicting gene expression from histone marks using chromatin deep learning models depends on histone mark function, regulatory distance and cellular states @NAR_Open 1. This study uncovers how gene expression prediction from histone marks is influenced by histone mark

A new study, led by Sarah Marzi (UK DRI at King’s) and Alan Murphy (UK DRI at Imperial), reveals key insight into how gene expression🧬is regulated in health and disease. The findings could be applied to identify influences on gene expression in neurodegenerative conditions,

We’re hiring! The Simons Center for Quantitative Biology at Cold Spring Harbor Laboratory is looking for a Research Technician to join our team! If you’re passionate about genomics and AI, we want to hear from you. Help spread the word! Please RT! jobrxiv.org/job/cold-sprin…

Have a genomics ML model and want to use it to design regulatory elements? tangermeme v0.4.3 is out with a focus on design! - faster (3-10x usually) - extended docs - evolutionary design - new construct-centric paradigm - QOL improvements github.com/jmschrei/tange…

In a big life update, I successfully defended my PhD thesis - massive thanks to my PI Nathan Skene and assessors @steinaerts and Tom Ellis. Also thrilled to share that I will be joining Peter Koo at CSHL in the new year for a post-doc improving genomic deep learning models!

Just in time for the holidays, we are thrilled to give you the latest preprint from our lab: Unique nigral and cortical pathways implicated by epigenomic and transcriptional analyses in a rotenone rat model of Parkinson's disease doi.org/10.1101/2024.1…

If you know anyone who's looking for a PhD in neurodegenerative diseases with a focus on data science and epidemiology, this scheme looks great. I would be happy to supervise projects in the Global Health Innovation stream. liss-dtp.ac.uk/applying-to-li…

0/ Essential reading for anyone training or using sequence-function models trained on genomic sequences! 🚨 In our new preprint, we explore the ways homology within genomes can cause leakage when training sequence-based models and ways to prevent it

❤️‍🔥 Passionate about gene regulation research, wet/dry lab? 🎡 Excited about London's vibrant research and social scene? 🔬 Looking for a funded postdoc position (+ ideally, have the CV & drive to apply for a fellowship in your 1st year)? Please DM/email me to discuss!

We're so thrilled to finally share with you the published version of our CUT&Tag optimization and benchmarking paper "CUT&Tag recovers up to half of ENCODE ChIP-seq histone acetylation peaks" - out in Nature Communications today: nature.com/articles/s4146…

The premier conference on Machine Learning for Computational Biology is Sep 9-10 at the NY Genome Center in NYC! Submission deadline is June 1 for 2-page abstracts and 8-page papers (eligible for proceedings track). Registration is now open! (Link below) Please retweet!

Excited to share an update to D3 (DNA Discrete Diffusion) — an application of score-entropy discrete diffusion model for regulatory genomics! 🧬 Paper: biorxiv.org/content/10.110… (See thread below 👇) (1/n)

Our work on "Evaluating the representational power of pre-trained DNA language models for regulatory genomics" led by Amber Tang with help from Nirali Somia & Steven Yu is finally published in Genome Biology! Check it out! genomebiology.biomedcentral.com/articles/10.11…

I wrote about how genetic risk works in the context of embryo selection and how people often think about it all wrong. A short 🧵:

🚨New paper 🚨 Can protein language models help us fight viral outbreaks? Not yet. Here’s why 🧵👇 1/12

2025 Machine Learning in Computational Biology (#MLCB) meeting starts TODAY (9/10) at 9:30 (EST)! We have a great lineup of keynotes, contributed talks, and posters today and tomorrow! Schedule: mlcb.org/schedule Join for free via livestream: m.youtube.com/@mlcbconf

Next talk by Alan Murphy and Masayuki (Moon) Nagai (from my lab!) who are talking about how naive fine-tuning genomic DNNs leads to catastrophic forgetting and propose *iterative causal refinement* to improve learned associations to causal understanding of cis-regulatory biology!