Mikael Bodén A

Fluxion generates enzyme function predictions by modeling electron flow trajectories, enabling function generation beyond static classification.

This study identifies latent regulatory programs from disease-associated variants to reveal biological mechanisms and clinical subtypes in complex diseases.

TreeGazer selects informative protein sequences using phylogenetic structure and Bayesian optimization, outperforming alternative strategies in simulated campaigns and low-data settings.

The paper identifies systematic regional bias in ChIP-seq data across multiple experiments and proposes metrics to improve data integration.

This paper identifies Vgll2 and Tead1 as key regulators of Hcrt neuron generation in mouse and human, showing their role in differentiation and potential for cell therapy.

The paper introduces a framework using MSA-Transformer to generate hybrid proteins between specified source and target proteins, producing sequences with higher consistency and plausibility than random baselines.

This study evaluates how MSA Transformer captures evolutionary distance and epistasis in protein sequences, showing it can reconstruct phylogenetic trees consistent with maximum likelihood methods.

Squidly predicts catalytic residues using a biology-informed contrastive learning framework, outperforming existing methods and integrating with BLAST for efficient annotation.

DNACipher predicts variant effects across cell types and assays, identifying imVars at most GWAS loci with support from experimental data.

The paper shows that codon-level scores from the CaLM model provide additional information for missense variant interpretation beyond protein-level representations.

This study evaluates protein language models' ability to infer phylogenetic relationships without explicit training, finding they perform well in low- and high-gap settings and capture broader evolutionary relationships.

The paper identifies regions of the human genome under cellular constraint by analyzing chromatin accessibility across cell types, showing their predictive power for functional loci and applications in variant identification and drug discovery.

This paper introduces a Mixed-Integer Programming approach to optimally reconstruct indel events in phylogenetic trees, outperforming existing methods in accuracy and efficiency.

Cytocipher identifies significant cell populations in scRNA-seq data by statistically determining transcriptionally distinct clusters, revealing new cell types and subpopulations across various biological contexts.

The paper introduces TRIAGE-Cluster and TRIAGE-ParseR to define cell diversity in scRNA-seq data using epigenetic data as a biological anchor.

The paper introduces GRASP, a method for ancestral sequence reconstruction that handles large datasets and indels, demonstrating its ability to generate biologically active enzyme ancestors.

The paper creates an iPSC differentiation atlas and identifies TMEM88 as a regulator of mesendodermal development and blood pressure.

This study shows that while most hypothalamic neurons develop normally without the H3K27me3 epigenetic mark, specific neuronal subtypes are sensitive to its loss.

The paper investigates the role of PRC2 in mouse CNS development by integrating transcriptomic and epigenetic data to reveal layered regulatory mechanisms.

ChIP-R improves the reproducibility of ChIP-seq and ATAC-seq peak calls by statistically combining replicates, outperforming existing methods in recovering transcription factor binding sites.

This paper develops an evolutionary model of protein secondary structure to reveal biological relationships not evident in amino acid-based analysis.

T-Gene predicts target genes of transcription factors by combining histone modification correlation and genomic distance, achieving high positive predictive value.