Super lucky to get the Faraday!@royalsociety Very grateful for the support from @JohnInnesCentre, @CarolineDeanLab, @DingLab_RNA, and all the collaborators/friends🙏 https://t.co/n14xIImUNU

Microscopic “flowers” made from crystals and DNA open and close as the pH of the surrounding solution changes. Learn more in #ScienceAdviser: https://t.co/OikkKpB8FB 📥 Sign up for the daily #newsletter: https://t.co/mcqNeQWWFg

Cool! Congratulations!

1/ Excited to share our new study with @Brumbaugh_JB, now out in @NatureBiotech! P-bodies selectively sequester RNAs encoding cell fate regulators, often from the preceding developmental stage. Releasing these RNAs can drive changes in cell identity. 🧵 https://t.co/D7fnkJgNQ6

We're thrilled to announce SeqHub, an AI-enabled platform for biological sequence analysis. SeqHub brings together sequence search, genome annotation, and data sharing in one place. I dreamed of a single place where I could learn everything about my sequences. Today, a much more

Plant AI scientist 😃 Come and chat with it! https://t.co/nKfsSDHkc3 @HaopengYu @JohnInnesCentre

Rather than static database, we've made an AI plant scientist that could continuously learn new knowledge😃Hope it will help the plant science community😃 @HaopengYu @JohnInnesCentre @TheSainsburyLab @DingLab_RNA @UniofExeter #AIscientist

Proteins as sentences: Language models for learning protein-protein interactions. Proteins rarely act alone. Every biological process — signaling, replication, immunity — is built from interactions between proteins. Yet, while we can now predict the 3D structure of a single

Accurate protein structure determination from cryo-EM maps using deep learning and structure prediction 1. A new method named EMProt is introduced, which integrates deep learning and structure prediction to accurately determine protein structures from cryo-EM maps. This method

RiboPO: Preference Optimization for Structure and Stability-Aware RNA Design 1. RiboPO is a novel framework that tackles the complex challenge of RNA sequence design. It optimizes RNA sequences to ensure they adopt specific 3D structures while maintaining thermodynamic

omicML: An Integrative Bioinformatics and Machine Learning Framework for Transcriptomic Biomarker Identification https://t.co/sh6hD2ymMG #biorxiv_bioinfo

Can a single AI model design proteins, peptides, and nanobodies that bind nearly any biological target with nanomolar affinity?@MIT "BoltzGen: Toward Universal Binder Design" • Computational protein design methods have historically been limited to specific molecular classes

Epigenetics Update - Deciphering histone mark-specific fine-scale chromatin organization at high resolution with Micro-C-ChIP https://t.co/B2XqNaf8ZU #Epigenetics #Chromatin --- Discover the breakthrough at https://t.co/WmSYDGXnzb

velotest: Statistical assessment of RNA velocity embeddings reveals quality differences for reliable trajectory visualizations https://t.co/wSZ8tpChsQ

Congratulations to Huakun Zhang's group! #plantlncRNA

Big congratulations, Kelly!

#Paper2Agent is highlighted by @AnthropicAI as one of the impactful partnerships using Claude! 🚀

Missense variants in human forkhead transcription factors reveal determinants of forkhead DNA bispecificity

Sugar coats shield immunogenic RNA modification:A new function for RNA glycosylation https://t.co/fZpiOlTpUD

Preprint from @GoogleDeepMind & @Yale Scaling Large Language Models for Next-Generation Single-Cell Analysis https://t.co/PxcX6YdyIp Cell2Sentence-Scale (C2S-Scale) is a 27 billion parameter foundation model that takes scRNA analyses to the next level 🤯 https://t.co/dYdMBLnrGN

Large Language Models Meet Virtual Cell: A Survey 1. This comprehensive review explores how large language models (LLMs) are transforming cellular biology by enabling the development of “virtual cells” — computational systems that represent, predict, and reason about cellular