Bioinformatics

A multi-gene predictive model for the radiation sensitivity of nasopharyngeal carcinoma based on machine learning

1. Kailai Li
2. Junyi Liang
3. Nan Li
4. Jianbo Fang
5. Xinyi Zhou
6. Jian Zhang
7. Anqi Lin
8. Peng Luo
9. Hui Meng

• Curated by eLife

  eLife Assessment

  The authors have developed a robust machine learning approach to predict radio sensitivity in patients with NPC based on a defined gene signature. Some key aspects of this signature have been validated in vitro using relevant cell lines which strengthens the conclusions of this important and convincing study. The publication will be of interest to clinicians working on this indication as well as a more broader readership made up of scientists working on radiation biology and those with a bioinformatics/machine learning background.

Reviewed by eLife

Deep generative design of neutralizing nanobodies against SARS-CoV-2 variants

1. Liyun Huo
2. Tian Tian
3. Yanqin Xu
4. Qin Qin
5. Xinyi Jiang
6. Qiang Huang

Reviewed by Arcadia Science

Integer programming framework for pangenome-based genome inference

1. Ghanshyam Chandra
2. Md Helal Hossen
3. Stephan Scholz
4. Alexander T Dilthey
5. Daniel Gibney
6. Chirag Jain

Reviewed by Arcadia Science

BioFunctional: A Comprehensive App for Interpreting and Visualizing Functional Analysis of KEGG Pathways and Gene Ontologies

1. Alejandro Rodriguez
2. Antonio Monleon Getino

Reviewed by Arcadia Science

Signature Distance: Generalizing Energy Statistics

1. Nicolo' Lazzaro
2. Raffaele Marchesi
3. Gianluca Leonardi
4. Jacopo Tessadori
5. Marco Chierici
6. Gabriele Sales
7. Monica Moroni
8. Toma Tebaldi
9. Giuseppe Jurman

Reviewed by Arcadia Science

InteracTor: A new integrative feature extraction toolkit for improved characterization of protein structural properties

1. Jose Cleydson F. Silva
2. Layla Schuster
3. Nick Sexson
4. Matias Kirst
5. Marcio F. R. Resende
6. Raquel Dias

Reviewed by Arcadia Science

Machine Learning-Enhanced Drug Discovery for BACE1: A Novel Approach to Alzheimer's Therapeutics

1. Satyam Sangeet

Reviewed by PREreview

Building a community-driven bioinformatics platform to facilitate Cannabis sativa multi-omics research

1. Locedie Mansueto
2. Tobias Kretzschmar
3. Ramil Mauleon
4. Graham J. King

• Curated by GigaByte

  Editors Assessment:

  This paper reports the establishment of the International Cannabis Genomics Research Consortium (ICGRC) web portal leveraging the open source Tripal platform to enhance data accessibility and integration for Cannabis sativa (Cannabis) multi-omics research. With the aim of bringing together the wealth of publicly available genomic, transcriptomic, proteomic, and metabolomic data sets to improve cannabis for food, fiber and medicinal traits. Tripal is a content management system for genomics data, presenting a ready-to-use specialized ‘omics modules for loading, visualization, and analysis, and is GMOD (Generic Model Organism Database) standards-compliant. The paper explaining how this was put together, what data and features are available, and providing a case study for other communities wanting to create their own Tripal platform. Covering their setup and customizations of the Tripal platform, and how they re-engineered modules for multi-omics data integration, and addition of many other custom features that can be reused. Peer review fixed a few minor bugs and added clarifications on how the platform will be updated.

  *This evaluation refers to version 1 of the preprint

Reviewed by GigaByte

Unveiling the influence of tumor and immune signatures on immune checkpoint therapy in advanced lung cancer

1. Nayoung Kim
2. Sehhoon Park
3. Areum Jo
4. Hye Hyeon Eum
5. Hong Kwan Kim
6. Kyungjong Lee
7. Jong Ho Cho
8. Bo Mi Ku
9. Hyun Ae Jung
10. Jong-Mu Sun
11. Se-Hoon Lee
12. Jin Seok Ahn
13. Jung-Il Lee
14. Jung Won Choi
15. Dasom Jeong
16. Minsu Na
17. Huiram Kang
18. Jeong Yeon Kim
19. Jung Kyoon Choi
20. Hae-Ock Lee
21. Myung-Ju Ahn

• Curated by eLife

  eLife Assessment

  The authors utilized single-cell RNA-seq profiling of non-small cell lung cancer (NSCLC) patient tumor samples to generate useful insights into the determinants of immune checkpoint inhibitor (ICI) responsiveness in NSCLC patients. While some of the findings add weight to the current literature, the analysis is incomplete due to the small cohort size and heterogeneous population which has limited their ability to draw statistically supported conclusion after adjusting for multiple hypothesis testing, as well as the lack of functional characterization of the findings. This study would benefit from external cohorts to both validate the findings and justify the statistical analysis undertaken.

Reviewed by eLife

Coenzyme-protein interactions since early life

1. Alma Carolina Sanchez Rocha
2. Mikhail Makarov
3. Lukáš Pravda
4. Marian Novotný
5. Klára Hlouchová

• Curated by eLife

  eLife Assessment

  This study presents a valuable examination of the prevalence of interactions between amino acids from different periods of Earth's history and coenzymes. While the premise of this work is well founded and the analysis is solid, with more data, the interpretation could change. This manuscript would be of interest to evolutionary biologists and biophysicists.

Reviewed by eLife