Excited to attend and present at #ISMB2024 ISCB News! Make sure to check out our presentation during the CompMS section on July 13th! We'll be discussing waveome: a novel method for longitudinal metabolomics data analysis!

Great poster by ⁦Jacob Nearing⁩ ⁦hutlab⁩ presenting PARATHAA at #ISMB2024!

Interested in pursuing a PhD in Health Data Science? Join The George Washington University- Milken Institute School of Public Health for an info session on July 23rd, 12-1pm on Zoom. RSVP today! docs.google.com/forms/d/e/1FAI…

📢 Enjoying #JSM2024 so far? Top it off with our exciting invited panel! Join us today from 10:30 AM - 12:20 PM PDT (**see updated poster**)! Hope to see you all there! 😊 #Statistics #Biostatistics #DataScience #ChatGPT #LLMs

Excited about our lab's contribution to #JSM2024, presenting our progress on omics AI: a talk on the dynamics of longitudinal omics using #waveome and a panel on the future of LLMs (omics LLMs for health), organized by Himel Mallick, PhD, FASA (He/him) and Frauke Kreuter, and moderated by Siyuan Ma 🙏

Our latest plate-based assay yielded some delicious results 🔬🥞

Time to culture some great connections at the Cold Spring Harbor Laboratory's Microbiome meeting! Our lab's gut instinct told us this was the perfect place to share our findings :) 🦠🧬

On our way to #cshldata24 at Cold Spring Harbor Laboratory! Make sure to catch @chiraaggohel's talk on tools for metabolomics based epidemiology, and his poster on software for the integration of large scale metabolomics experiments! #metabolomics #bioinformatics

Excited to co-lead this tutorial with The Rahnavard Lab | GW at #ENAR2025! 🎉 Looking forward to engaging discussions on cutting-edge statistical approaches in metabolomics 🔬📊. See you on March 24 at 8:30 AM! 🚀🔬 #Omics #DataScience #Metabolomics #Statistics #Biostatistics

seqLens: optimizing language models for genomic predictions biorxiv.org/content/10.110… #biorxiv_bioinfo

seqLens: Optimizing Language Models for Genomic Predictions 1. The paper introduces seqLens, a family of DNA language models designed for genomic predictions, leveraging disentangled attention with relative positional encoding to enhance performance across multiple genomic

🧪🦠 𝐓𝐡𝐞 𝐜𝐚𝐧𝐜𝐞𝐫 𝐦𝐢𝐜𝐫𝐨𝐛𝐢𝐨𝐦𝐞 𝐜𝐨𝐧𝐭𝐫𝐨𝐯𝐞𝐫𝐬𝐲 𝐢𝐬 𝐟𝐢𝐧𝐚𝐥𝐥𝐲 𝐛𝐞𝐡𝐢𝐧𝐝 𝐮𝐬 🧠🧬 What began as a side project at Merck evolved into a full-scale discovery effort — a nearly two-year meta-analysis led by Xinyang Zhang and the The Rahnavard Lab | GW Lab,

Antibiotic resistance is outpacing traditional detection methods. Led by Matthew Mollerus, we introduce resLens, a family of genomic language models that learn latent DNA representations to detect and analyze ARGs without rigid reference databases.