Corrigendum: Differential co-expression network analysis reveals key hub-high traffic genes as potential therapeutic targets for COVID-19 pandemic
Bibliographic record
Abstract
Name of all authors as they appear in the published original article Aliakbar Hasankhani1*†, Abolfazl Bahrami1,2*†, Negin Sheybani3, Behzad Aria4, Behzad Hemati5, Farhang Fatehi1, Hamid Ghaem Maghami Farahani1, Ghazaleh Javanmard1, Mahsa Rezaee6, John P. Kastelic7 and Herman W. Barkema7Affiliations of all authors as they appear in the published original version of the article 1Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.2Nuclear Agriculture Research School, Nuclear Science and Technology Research Institute, Karaj, Iran.3Department of Animal and Poultry Science, College of Aburaihan, University of Tehran, Tehran, Iran.4Department of Physical Education and Sports Science, School of Psychology and Educational Sciences, Yazd University, Yazd, Iran.5Biotechnology Research Center, Karaj branch, Islamic Azad University, Karaj, Iran.6Department of Medical Mycology, School of Medical Science, Tarbiat Modares University, Tehran, Iran.7Department of Production Animal Health, Faculty of Veterinary Medicine, University of Calgary, Calgary, AB, Canada.)* Correspondence: A.hasankhani74@ut.ac.ir; a.bahrami@ut.ac.irKeywords: systems biology, systems immunology, WGCNA, hub-high traffic genes, immunopathogenesis, therapeutic targets in infectious diseases, COVID-19 pandemicCorrigendum on: Hasankhani A, Bahrami A, Sheybani N, Aria B, Hemati B, Fatehi F, Ghaem Maghami Farahani H, Javanmard G, Rezaee M, Kastelic JP and Barkema HW (2021) Differential Co-Expression Network Analysis Reveals Key Hub-High Traffic Genes as Potential Therapeutic Targets for COVID-19 Pandemic. Front. Immunol. 12:789317. doi: 10.3389/fimmu.2021.789317. Incorrect AffiliationIn the published article, there was an error in affiliation 2. Instead of “BiomedicalCenter for Systems Biology Science Munich, Ludwig-Maximilians-University, Munich, Germany”, it should be “Nuclear Agriculture Research School, Nuclear Science and Technology Research Institute, Karaj, Iran”. The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.152 | 0.058 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".