Bioinformatics

Comprehensive machine-learning survival framework develops a consensus model in large-scale multicenter cohorts for pancreatic cancer

1. Libo Wang
2. Zaoqu Liu
3. Ruopeng Liang
4. Weijie Wang
5. Rongtao Zhu
6. Jian Li
7. Zhe Xing
8. Siyuan Weng
9. Xinwei Han
10. Yu-ling Sun

• Curated by eLife

  Evaluation Summary:

  This work sets out to develop a better machine learning-based predictor of survival/prognosis for patients diagnosed with pancreatic cancer, by developing a large combinatorial family of machine learning methods based on a high-dimensional set of -omics and other patient data features; using ten publicly available data sets. A reduced set of features (giving rise to a signature called AIDPS that involves 9 genes) was identified. Unfortunately, the authors used all ten data sets both in the discover stage and in the validation stage of their study. There was also a large mismatch between the initial number of covariates (15,288 genes) and the number of samples (n=1280). The combinatorial ensemble of ML models makes for an unwieldy methodology that is difficult to interpret or duplicate.

  (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. Reviewer #2 agreed to share their name with the authors.)

Reviewed by eLife

CriSNPr, a single interface for the curated and de novo design of gRNAs for CRISPR diagnostics using diverse Cas systems

1. Asgar H Ansari
2. Manoj Kumar
3. Sajal Sarkar
4. Souvik Maiti
5. Debojyoti Chakraborty

• Curated by eLife

  Evaluation Summary:

  The web-based software developed in this study will be of interest to researchers who develop CRISPR-based diagnostic methods. The use of CRISPR-Cas to rapidly identify specific mutations in both cancer and infection is an evolving field with good potential to play a role in future research and diagnostics. This software will facilitate the implementation of such technologies and is therefore useful.

  (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. Reviewer #1 agreed to share their name with the authors.)

Reviewed by eLife

A practical tool for maximal information coefficient analysis

1. Davide Albanese
2. Samantha Riccadonna
3. Claudio Donati
4. Pietro Franceschi

Reviewed by GigaScience

Accurate prediction of virus-host protein-protein interactions via a Siamese neural network using deep protein sequence embeddings

1. Sumit Madan
2. Victoria Demina
3. Marcus Stapf
4. Oliver Ernst
5. Holger Fröhlich

Reviewed by ScreenIT

SARS-CoV-2 helicase might interfere with cellular nonsense-mediated RNA decay: insights from a bioinformatics study

1. Behnia Akbari
2. Ehsan Ahmadi
3. Mohammad Reza Zabihi
4. Mina Roshan Zamir
5. Mina Sadeghi Shaker
6. Farshid Noorbakhsh

Reviewed by ScreenIT

Monitoring for SARS-CoV-2 drug resistance mutations in broad viral populations

1. Mayya Sedova
2. Lukasz Jaroszewski
3. Mallika Iyer
4. Adam Godzik

Reviewed by ScreenIT

Annotation of Hox cluster and Hox cofactor genes in the Asian citrus psyllid, Diaphorina citri, reveals novel features

1. Teresa D. Shippy
2. Prashant S. Hosmani
3. Mirella Flores-Gonzalez
4. Lukas A. Mueller
5. Wayne B. Hunter
6. Susan J. Brown
7. Tom D’Elia
8. Surya Saha

Reviewed by GigaByte

Annotation of putative circadian rhythm-associated genes in Diaphorina citri (Hemiptera: Liviidae)

1. Max Reynolds
2. Lucas de Oliveira
3. Chad Vosburg
4. Thomson Paris
5. Crissy Massimino
6. Jordan Norus
7. Yasmin Ortiz
8. Michelle Espino
9. Nina Davis
10. Ron Masse
11. Alan Neiman
12. Rachel Holcomb
13. Kylie Gervais
14. Melissa Kemp
15. Maria Hoang
16. Teresa D. Shippy
17. Prashant S. Hosmani
18. Mirella Flores-Gonzalez
19. Kirsten Pelz-Stelinski
20. Jawwad A. Qureshi
21. Lukas A. Mueller
22. Wayne B. Hunter
23. Joshua B. Benoit
24. Susan J. Brown
25. Tom D’Elia
26. Surya Saha

Reviewed by GigaByte

Manual curation and phylogenetic analysis of chitinase family genes in the Asian citrus psyllid, Diaphorina citri

1. Teresa D. Shippy
2. Sherry Miller
3. Blessy Tamayo
4. Prashant S. Hosmani
5. Mirella Flores-Gonzalez
6. Lukas A. Mueller
7. Wayne B. Hunter
8. Susan J. Brown
9. Tom D’Elia
10. Surya Saha

Reviewed by GigaByte

Nomograms of human hippocampal volume shifted by polygenic scores

1. Mohammed Janahi
2. Leon Aksman
3. Jonathan M Schott
4. Younes Mokrab
5. Andre Altmann
6. On behalf of for the Alzheimer’s Disease Neuroimaging Initiative

• Curated by eLife

  Evaluation Summary:

  This manuscript considers whether genetic information can improve the clinical utility of population norms derived from brain imaging data. The authors propose to incorporate polygenic scores into normative models of hippocampal volume to improve predictions of neurodegenerative disease. This approach is elegantly demonstrated in this manuscript and may be useful for clinical translation of population neuroimaging.

  (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. Reviewer #1 and Reviewer #2 agreed to share their name with the authors.)

Reviewed by eLife