Rafael Josip Penić

Thrilled to share that our paper introducing RiNALMo is now published! Check it out 👇

HERRO has been published in @Nature !! nature.com/articles/s4158… This achievement is a result of the great work by @domstanojevic, with contributions from @DehuiLin, @sergeynurk, and @PaolaFlorezdeS We are entering the era of high-quality genome assemblies supported by AI. This is just the beginning.

Following up on this - MADRe is now officially published 🎉 Very grateful for the guidance of @msikic @r_vicedomini and @KrizanovicK 🔗 academic.oup.com/gigascience/ad…

🚀 Excited to share that our preprint is out! Huge thanks to my amazing collaborators @DobrikG, Marin Šilić, and @PetarV_93 ! KNARsack – a neural algorithmic reasoner for the Knapsack problem. 🎒🧠

@sarrabakic @BryanHooi1 @msikic Great work Sara!

I am happy to introduce Campolina, a deep neural framework that replaces traditional algorithmic approaches for nanopore signal segmentation and improves segmentation quality for real-time analysis. Preprint and details in the thread👇 K. Friganovic, @BryanHooi1, @msikic 1/7

@JosipaLipovac @msikic @r_vicedomini @KrizanovicK Great job Josipa!!!!!!

I am happy to share our new preprint introducing MADRe - a pipeline for Metagenomic Assembly-Driven Database Reduction, enabling accurate and computationally efficient strain-level metagenomic classification. @msikic, @r_vicedomini, @KrizanovicK 🔗biorxiv.org/content/10.110… 1/9

@JosipaLipovac @MileSikic @r_vicedomini @KrizanovicK Great job Josipa!!!

We are organising ai4na-workshop.github.io at#ICLR2025. Topics: RNA, DNA and cell LLMs, structure, modifications, correction, variant calling... Deadline: Feb 10th 2025 Time for revision: 1 month Accepted papers - the opportunity to be invited by Nature Methods for submission

Submit your papers to our workshop AI for Nucleic Acids workshop at ICLR 2025!

🚨 1 month to go! 🚨 The submission deadline for the AI4NA workshop at @iclr_conf is fast approaching! 🧬 ✨ Submissions on OpenReview will open soon—stay tuned! ✨ 🔗 Learn more on our web page (link below 👇) #AI4NA #ICLR2025

🚀Thrilled to be part of ICLR 2025! Join our workshop AI for Nucleic Acids (AI4NA) to explore cutting-edge research and connect with field leaders. Thanks to our organizers and @iclr_conf for making this possible. Find the link to the website in our bio! More info below👇

@JosipaLipovac Congratulations on your publication! 🎉 Looking forward to seeing the impact of your research!

So our paper is finally out! 🥳 Our findings aim to guide researchers on balancing cost, data, and quality in population-level human genome assembly projects. Check it here: rdcu.be/d36pr

RNA structure prediction is still an open problem. Look at our benchmark results (including Alphafold 3!) biorxiv.org/content/10.110… w/ @im50603 Tin Vlasic Yang Li @BryanHooi1 and Zhang Yang

A Comparative Review of Deep Learning Methods for RNA Tertiary Structure Prediction biorxiv.org/cgi/content/sh… #biorxiv_bioinfo

We are seeking talented PhD candidates in CS, physics or math to join us. The topics include the development of AI for cancer and ageing research. 📅 Application Deadline: 1st Dec 2024 Lab: sikic-lab.github.io Scholarship: a-star.edu.sg/Scholarships/f… Please share!

CoPRA: Bridging Cross-domain Pretrained Sequence Models with Complex Structures for Protein-RNA Binding Affinity Prediction - Curate large datasets of RNA-Protein pairs (PRI30K, 150K pairs) from BioLIP2, and an affinity dataset (PRA310, 435 complexes) from PDBBind, ProNAB, and PRBABv2 - Use ESM-2 650M and RiNALMo to encode protein and RNA. Pretrain the model with pair structure embeddings (including residue type, angular, and positional info) on Contrastive Loss of RNA-Protein pairs, and Interface Distance via masking - Evaluate on binding affinity prediction and mutation effect prediction with a special mask for each downstream task Preprint: arxiv.org/abs/2409.03773 Code: github.com/hanrthu/CoPRA/…

Our RNA LLM RiNALMo has received the prize for the most ambitious submission at the Machine Learning for Life and Material Science workshop at @icmlconf 2024. w/ @RJPenic Tin Vlasic @ywan_wan and Roland Huber. Workshop: ml4lms.bio Preprint: arxiv.org/abs/2403.00043

Rockfish: A transformer-based model for accurate 5-methylcytosine prediction from nanopore sequencing has been published in Nat. Comm!! Great work by @domstanojevic w/ Zhe Li, @sarrabakic and @rsyf2 Paper: nature.com/articles/s4146… Code: github.com/lbcb-sci/rockf…