Bioinformatics

Underlying selection for the diversity of spike protein sequences of SARS‐CoV ‐2

1. Manisha Ghosh
2. Surajit Basak
3. Shanta Dutta

Reviewed by ScreenIT

Multi-label topic classification for COVID-19 literature annotation using an ensemble model based on PubMedBERT

1. Shubo Tian
2. Jinfeng Zhang

Reviewed by ScreenIT

DynaMorph: self-supervised learning of morphodynamic states of live cells

1. Zhenqin Wu
2. Bryant B. Chhun
3. Galina Popova
4. Syuan-Ming Guo
5. Chang N. Kim
6. Li-Hao Yeh
7. Tomasz Nowakowski
8. James Zou
9. Shalin B. Mehta

Reviewed by Review Commons

Deep learning based on stacked sparse autoencoder applied to viral genome classification of SARS-CoV-2 virus

1. Maria G. F. Coutinho
2. Gabriel B. M. Câmara
3. Raquel de M. Barbosa
4. Marcelo A. C. Fernandes

Reviewed by ScreenIT

BugSplit enables genome-resolved metagenomics through highly accurate taxonomic binning of metagenomic assemblies

1. Induja Chandrakumar
2. Nick P. G. Gauthier
3. Cassidy Nelson
4. Michael B. Bonsall
5. Kerstin Locher
6. Marthe Charles
7. Clayton MacDonald
8. Mel Krajden
9. Amee R. Manges
10. Samuel D. Chorlton

Reviewed by ScreenIT

R346K Mutation in the Mu Variant of SARS-CoV-2 Alters the Interactions with Monoclonal Antibodies from Class 2: A Free Energy Perturbation Study

1. Filip Fratev

Reviewed by ScreenIT

SARS-CoV-2 PLpro whole human proteome cleavage prediction and enrichment/depletion analysis

1. Lucas Prescott

Reviewed by ScreenIT

Hierarchical Gaussian Processes and Mixtures of Experts to Model COVID-19 Patient Trajectories

1. Sunny Cui
2. Elizabeth C. Yoo
3. Didong Li
4. Krzysztof Laudanski
5. Barbara E. Engelhardt

Reviewed by ScreenIT

Characterization and Structural Prediction of ORF10, ORF7b, ORF7a, ORF6, Membrane Glycoprotein, and Envelope Protein in SARS-CoV-2 Bangladeshi Variant through Bioinformatics Approach

1. Pinky Debnath
2. Umama Khan
3. Md. Salauddin Khan

Reviewed by ScreenIT

A survey of optimal strategy for signature-based drug repositioning and an application to liver cancer

1. Chen Yang
2. Hailin Zhang
3. Mengnuo Chen
4. Siying Wang
5. Ruolan Qian
6. Linmeng Zhang
7. Xiaowen Huang
8. Jun Wang
9. Zhicheng Liu
10. Wenxin Qin
11. Cun Wang
12. Hualian Hang
13. Hui Wang

• Curated by eLife

  Evaluation Summary:

  This paper will be of interest to scientists across translational medicine and cancer treatment. It describes a standardized method to identify drugs that could be potentially repositioned for tumor treatment. In addition, using this new method and experimental manipulations the authors identify homoharringtonine as a new potential therapy for liver cancer and the underlying liver disease. However, while bioinformatic analysis was really comprehensive, the results and conclusions obtained are based on public datasets and therefore limited by the data available. In addition, the experimental approach to test the potential new treatments are currently based on in vitro assays and would be strengthened by in vivo validations.

  (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)

Reviewed by eLife