Bioinformatics

RWRtoolkit: multi-omic network analysis using random walks on multiplex networks in any species

1. David Kainer
2. Matthew Lane
3. Kyle A Sullivan
4. J Izaak Miller
5. Mikaela Cashman
6. Mallory Morgan
7. Ashley Cliff
8. Jonathon Romero
9. Angelica Walker
10. D Dakota Blair
11. Hari Chhetri
12. Yongqin Wang
13. Mirko Pavicic
14. Anna Furches
15. Jaclyn Noshay
16. Meghan Drake
17. A J Ireland
18. Ali Missaoui
19. Yun Kang
20. John C Sedbrook
21. Paramvir Dehal
22. Shane Canon
23. Daniel Jacobson

Reviewed by GigaScience

Defining the limits of plant chemical space: challenges and estimations

1. Chloe Engler Hart
2. Yojana Gadiya
3. Tobias Kind
4. Christoph A. Krettler
5. Matthew Gaetz
6. Biswapriya B. Misra
7. David Healey
8. August Allen
9. Viswa Colluru
10. Daniel Domingo-Fernández

Reviewed by GigaScience

V-pipe 3.0: a sustainable pipeline for within-sample viral genetic diversity estimation

1. Lara Fuhrmann
2. Kim Philipp Jablonski
3. Ivan Topolsky
4. Aashil A Batavia
5. Nico Borgsmüller
6. Pelin Icer Baykal
7. Matteo Carrara
8. Chaoran Chen
9. Arthur Dondi
10. Monica Dragan
11. David Dreifuss
12. Anika John
13. Benjamin Langer
14. Michal Okoniewski
15. Louis du Plessis
16. Uwe Schmitt
17. Franziska Singer
18. Tanja Stadler
19. Niko Beerenwinkel

Reviewed by GigaScience

Network-based anomaly detection algorithm reveals proteins with major roles in human tissues

1. Dima Kagan
2. Juman Jubran
3. Esti Yeger-Lotem
4. Michael Fire

Reviewed by GigaScience

Steamboat: Attention-based multiscale delineation of cellular interactions in tissues

1. Shaoheng Liang
2. Junjie Tang
3. Guanghan Wang
4. Jian Ma

Reviewed by preLights

Unsupervised Representation Learning of C. elegans Poses and Behavior Sequences From Microscope Video Recordings

1. Maurice Deserno
2. Katarzyna Bozek

• Curated by eLife

  eLife Assessment

  This valuable study introduces a self-supervised machine learning method to classify C. elegans postures and behaviors directly from video data, offering an alternative to the skeleton-based approaches that rely on often error-prone tracking. This novel approach holds promise for advancing ethology research. That said, the strength of evidence is currently incomplete, as key aspects - including measuring head-tail orientation, increased behavioral interpretability, and quantitative comparisons to established methods - are underdeveloped and would benefit from further validation.

Reviewed by eLife

Artificial intelligence driven tumor risk stratification from single-cell transcriptomics using phenotype algebra

1. Namrata Bhattacharya
2. Anja Rockstroh
3. Sanket Suhas Deshpande
4. Sam Koshy Thomas
5. Anunay Yadav
6. Chitrita Goswami
7. Smriti Chawla
8. Pierre Solomon
9. Cynthia Fourgeux
10. Gaurav Ahuja
11. Brett Hollier
12. Himanshu Kumar
13. Antoine Roquilly
14. Jeremie Poschmann
15. Melanie Lehman
16. Colleen C Nelson
17. Debarka Sengupta

• Curated by eLife

  eLife Assessment

  This manuscript presents an important contribution to the field of single-cell transcriptomic analysis in cancer by introducing a novel computational framework-SCellBOW-which applies embedding techniques from natural language processing to model phenotypic heterogeneity in tumors. The revised version includes new validation experiments and significant clarifications that provide convincing evidence for the method's utility. The authors have benchmarked SCellBOW across diverse datasets, including glioblastoma, breast, and metastatic prostate cancer, and have demonstrated its superior performance compared to existing state-of-the-art methods.

Reviewed by eLife

Decoding Liver Cancer Prognosis: From Multi-omics Subtypes, Prognostic Models to Single Cell Validation

1. Yanbin Wang
2. Yuqi Wu
3. Hong Zhang
4. Xinyue Liu
5. Jing Ling
6. Xiao Zhou
7. Anping Song
8. Li Sun
9. Hong Qiu
10. Xianglin Yuan
11. Hua Xiong
12. Yanmei Zou

• Curated by eLife

  eLife Assessment

  This important revised manuscript presents compelling findings by delineating two molecularly distinct liver cancer subtypes through comprehensive multi-omics integration and constructing a rigorously validated prognostic model. The authors have strengthened the analytical framework and validation across multiple datasets, including single-cell RNA sequencing. The evidence remains robust, with enhanced methodological clarity and expanded validation in both internal and independent cohorts. The revisions have improved the study's rigor and translational relevance.

Reviewed by eLife

Machine learning ensemble identifies distinct age-related response to spaceflight in mammary tissue

1. James A. Casaletto
2. Tyler Zhao
3. Jay Yeung
4. Amaan Ansari
5. Ayush Raj
6. Arnav Mishra
7. Amber Fry
8. Kathryn Sun
9. Sofia Lendahl
10. Willy Guan
11. Abigail Lee
12. Lauren M. Sanders
13. Sylvain V. Costes

Reviewed by PREreview

Hidden GPCR structural transitions addressed by multiple walker supervised molecular dynamics (mwSuMD)

1. Giuseppe Deganutti
2. Ludovico Pipito
3. Roxana Maria Rujan
4. Tal Weizmann
5. Peter Griffin
6. Antonella Ciancetta
7. Stefano Moro
8. Christopher Arthur Reynolds

• Curated by eLife

  eLife Assessment

  This study describes an improved adaptive sampling approach, multiple-walker Supervised Molecular Dynamics (mwSuMD), and its application to G protein-coupled receptors (GPCRs), which are the most abundant membrane proteins and key targets for drug discovery. The manuscript provides solid evidence that the mwSuMD approach can assist in the sampling of complex binding processes, leading to useful findings for GPCR activity, including resolution of interactions not seen experimentally. The method has the potential to have broad applicability in structural biology and pharmacology.

Reviewed by eLife