ArticleBioinformation2018
Future Innovations in Viral Immune Surveillance: A Novel Place for Bioinformation and Artificial Intelligence in the Administration of Health Care.
Article in Bioinformation, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 20 citations in OpenAlex.
- Artificial Intelligence in Public Health Education: A Scoping Review of Workforce Competency Development.Health science reports · 2026Article
- Permafrost viremia and immune tweening.Bioinformation · 2023Article
- Contemporary Role and Applications of Artificial Intelligence in Dentistry.F1000Research · 2023Review
- Effective prediction of potential ferroptosis critical genes in clinical colorectal cancer.Frontiers in oncology · 2022Article
- Toward a fractalomic idiotype/anti-idiotypic paradigm.Bioinformation · 2022Article
- Permafrost Immunity.Bioinformation · 2022Article
- Coronavirus disease 2019 (COVID-19): An overview of the immunopathology, serological diagnosis and management.Scandinavian journal of immunology · 2021Review
- The Constant Threat of Zoonotic and Vector-Borne Emerging Tropical Diseases: Living on the Edge.Frontiers in tropical diseases · 2021Article
- 2019-nCoV - Towards a 4th generation vaccine.Bioinformation · 2020Article
- CoViD-19 Immunopathology and Immunotherapy.Bioinformation · 2020Article
- Putative Natural History of CoViD-19.Bioinformation · 2020Article
- Bioinformation Informs the Allostasiome: Translational Environmental Restoration (TER) for the Climate Crisis Medical Emergency.Bioinformation · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Novel developments in bioinformation, bioinformatics and biostatistics, including artificial intelligence (AI), play a timely and critical role in translational care. Case in point, the extent to which viral immune surveillance is regulated by immune cells and soluble factors, and by non-immune factors informs the administration of health care. The events by which health is regained following viral infection is an allostatic process, which can be modeled using Hilbert's and Volterra's mathematical biology criteria, and biostatistical methodologies such as linear multiple regression. Health regained following viral infection can be given as Y being the sum-total of the positive factors and events (∏) that inherently push allostasis forward (i.e., the orderly process of immune activation and maturation) and the negative (N) factors and events that, allostatically speaking, interfere with regaining health. Any gaps in knowledge are filled by AI-aided immune tweening. Proof of concept can be tested with the fast-gaining infection using tick-borne Bunyavirus that cause severe fever with thrombocytopenia syndrome (SFTS).
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.