Editorial: Computational systems immunovirology
Bibliographic record
Abstract
Computational systems immunovirologyVirus-immune system interplay has a significant effect on the final fate of infection.Therefore, it is essential to survey the pathogenesis mechanism of viral-caused disease through the alteration of the immune system function.One of the most effective approaches to surveying biological processes is systems biology, in which the whole system is studied rather than the individual parts (1, 2).Computational systems biology and machine/deep learning methods rely on mathematical and statistical algorithms as well as the modeling of biological systems (3, 4).These computational approaches can be employed in the analysis of immune-related data.This "Research Topic" highlights eight articles that have been published in "Frontiers in Immunology".They aimed to utilize computational systems immunovirology for the following purposes: i) analysis of immunogenomics data for various aims; ii) developing a prediction model to find genome-scale protein-protein interactions between various virus strains and human proteome; iii) developing an online website to forecast the interplays among antigens and antibodies; iv) utilizing nonlinear ordinary differential equations to model the antibody dynamics; v) developing a multiscale mechanistic model for human Dendritic cells, vi) introducing a multilevel adapted prediction approach to detect the antiviral T cells produced by virotherapy; and viii) introducing a deep learning method to describe the gathered effects of multiple vaccine design variables.Tumor growth can be impressed by viruses through particular target genes.Liao et al. evaluate the communication between the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and malignancies.This was based on the idea that the immune system might be amplified by RNA vaccines of SARS-CoV-2 to destroy cancer (5).The authors assess the methylation, genomic mutation, clinical characteristics, immunological characteristics of SARS-CoV-2 target Genes (STGs), and activation of signature-related pathways in solid tumors.Furthermore, they construct risk prognostic models that rely on STGs, and their communications with immunology were identified among 33 types of tumors.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.039 | 0.022 |
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".