Bioinformatics

The Virtual Lab: AI Agents Design New SARS-CoV-2 Nanobodies with Experimental Validation

1. Kyle Swanson
2. Wesley Wu
3. Nash L. Bulaong
4. John E. Pak
5. James Zou

Reviewed by PREreview

Multivariate Mutual Information based Feature Selection for Predicting Histone Post-Translational Modifications in Epigenetic Datasets

1. V. K. Dhanasekhar
2. Sibi Raj B. Pillai
3. Nithya Ramakrishnan

Reviewed by Arcadia Science

The role of induced polarization in drug discovery applications

1. Ashraf Mohamed
2. Bernard R. Brooks
3. Muhamed Amin

Reviewed by PREreview

Have protein-ligand co-folding methods moved beyond memorisation?

1. Peter Škrinjar
2. Jérôme Eberhardt
3. Janani Durairaj
4. Torsten Schwede

Reviewed by PREreview, Arcadia Science

AI-Guided Discovery and Optimization of Antimicrobial Peptides Through Species-Aware Language Model

1. Daehun Bae
2. Minsang Kim
3. Jiwon Seo
4. Hojung Nam

Reviewed by Arcadia Science

From Likelihood to Fitness: Improving Variant Effect Prediction in Protein and Genome Language Models

1. Charles W. J. Pugh
2. Paulina G. Nuñez-Valencia
3. Mafalda Dias
4. Jonathan Frazer

Reviewed by Arcadia Science

External validation of machine learning models—registered models and adaptive sample splitting

1. Giuseppe Gallitto
2. Robert Englert
3. Balint Kincses
4. Raviteja Kotikalapudi
5. Jialin Li
6. Kevin Hoffschlag
7. Ulrike Bingel
8. Tamas Spisak

Reviewed by GigaScience

Spatial Integration of Multi-Omics Data using the novel Multi-Omics Imaging Integration Toolset

1. Maximillian Wess
2. Maria K. Andersen
3. Elise Midtbust
4. Juan Carlos Cabellos Guillem
5. Trond Viset
6. Øystein Størkersen
7. Sebastian Krossa
8. Morten Beck Rye
9. May-Britt Tessem

Reviewed by GigaScience

Integrative Analysis of Neuroimaging and Microbiome Data Predicts Cognitive Decline in Parkinson’s Disease

1. Büşranur Delice
2. Özkan Ufuk Nalbantoğlu
3. Süleyman Yıldırım

Reviewed by PREreview

Predicting the effect of CRISPR-Cas9-based epigenome editing

1. Sanjit Singh Batra
2. Alan Cabrera
3. Jeffrey P Spence
4. Jacob Goell
5. Selvalakshmi S Anand
6. Isaac B Hilton
7. Yun S Song

• Curated by eLife

  eLife Assessment

  This study presents an advance in efforts to use histone post-translational modification (PTM) data to model gene expression and to predict epigenetic editing activity. Such models are broadly useful to the research community, especially ones that can model and predict epigenetic editing activity, which is novel; additionally, the authors have nicely integrated datasets across cell types into their model. The work is mostly solid, but it would be strengthened by performing further comparisons to existing methods that predict gene expression from PTM data and from more comprehensive functional validation of model-predicted epigenome editing outcomes beyond dCas9-p300 based perturbations. This work will be of interest to the epigenetics and computational modeling communities.

Reviewed by eLife