Bioinformatics

Pathway activation model for personalized prediction of drug synergy

1. Quang Thinh Trac
2. Yue Huang
3. Tom Erkers
4. Päivi Östling
5. Anna Bohlin
6. Albin Osterroos
7. Mattias Vesterlund
8. Rozbeh Jafari
9. Ioannis Siavelis
10. Helena Backvall
11. Santeri Kiviluoto
12. Lukas Orre
13. Mattias Rantalainen
14. Janne Lehtiö
15. Soren Lehmann
16. Olli Kallioniemi
17. Yudi Pawitan
18. Trung Nghia Vu

• Curated by eLife

  eLife Assessment

  This valuable study presents a deep learning framework for predicting synergistic drug combinations for cancer treatment in the AstraZeneca-Sanger (AZS) DREAM Challenge dataset. The level of evidence seems solid, although performance on some datasets seems unconvincing and further validation would be required to demonstrate the generalizability of the model and, in turn, its clinical relevance. The reported tool, DIPx, could be of use for personalized drug synergy prediction and exploring the activated pathways related to the effects of drug combinations.

Reviewed by eLife

Drug combination prediction for cancer treatment using disease-specific drug response profiles and single-cell transcriptional signatures

1. Daniel Osorio
2. Parastoo Shahrouzi
3. Xavier Tekpli
4. Vessela N Kristensen
5. Marieke Lydia Kuijjer

• Curated by eLife

  eLife Assessment

  The study conducted by Hurtado et al. offers important insights and solid evidence regarding the prediction of drug combinations for cancer treatment. By leveraging disease-specific drug response profiles and single-cell transcriptional signatures, this research not only demonstrates a novel and effective approach to identifying potential drug synergies but it also enhances our understanding of the underlying mechanisms of drug response prediction.

Reviewed by eLife

A comprehensive antigen-antibody complex database unlocking insights into interaction interface

1. Yuwei Zhou
2. Wenwen Liu
3. Ziru Huang
4. Yushu Gou
5. Siqi Liu
6. Lixu Jiang
7. Yue Yang
8. Jian Huang

• Curated by eLife

  eLife Assessment

  This useful manuscript provides a newly curated database (termed AACDB) of antibody-antigens structural information, alongside annotations that are either taken and from the PDB, or added de-novo. Sequences, structures, and annotations can be easily downloaded from the AACDB website, speeding up the development of structure-based algorithms and analysis pipelines to characterize antibody-antigen interactions. The methodology presented for this data curation is solid. The curated dataset will be of broad interest and value to researchers interested in antibody-antigen interactions.

Reviewed by eLife

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

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