New in Briefings in Bioinformatics: we use the Gene Ontology @geneontology.bsky.social as a logical framework to ask whether genome-scale function annotations are biologically plausible, not only accurate protein by protein. #GeneOntology #Bioinformatics 1/6

New in Bioinformatics: INDIGENA, an inductive method to predict disease-gene associations by ranking genes for a set of phenotypes. 🧵 1/5

3 papers from our group accepted at #ESWC2026 in Dubrovnik next week! Excited to present work on drug-safety knowledge graphs, geometric multi-hop reasoning over KGs, and robust KG embeddings via denoising. See you in Dubrovnik!

We made three pangenome graphs 🧬 public, one for the Japanese, one for the Saudi population, and a merged graph (JaSaPaGe). Useful for 🖥️ bioinformatics on either population, or to evaluate how pangenome graphs behave when two different populations are included. jasapage.bio2vec.net/view for PanGene

Phased genome assemblies and pangenome graphs of human populations of Japan and Saudi Arabia

The selection of a reference sequence in genome analysis is critical, as it serves as the foundation for all downstream analyses. Recently, the pangenome graph has been proposed as a data model that i...

biorxiv.org

ProtBoost: protein function prediction with Py-Boost and Graph Neural Networks -- CAFA5 top2 solution The second-place solution in CAFA5 has now been published. Paper: arxiv.org/abs/2412.045... GitHub Repo: github.com/btbpanda/CAF... Kaggle Writeup: www.kaggle.com/competitions...

Proud to share our new paper! A complete genome from Saudi Arabia (KSA001), freely available to all. Complex work - not just sequencing & assembly challenges, but also navigating IRB approval to ensure ethical data sharing & open science principles. nature.com/articles/s41...

A reference quality, fully annotated diploid genome from a Saudi individual - Scientific Data

Scientific Data - A reference quality, fully annotated diploid genome from a Saudi individual

nature.com

I am excited to share that our paper on creating a very large structure causal model for diseases has been published. We generate an SCM containing most common diseases, and validate with #UKBiobank data, for better polygenic scores, and finding pleitropic variants academic.oup.com/bioinformati...

Causal relationships between diseases mined from the literature improve the use of polygenic risk scores

AbstractMotivation. Identifying causal relations between diseases allows for the study of shared pathways, biological mechanisms, and inter-disease risks.

academic.oup.com