BACK TO CONTENTS   |    PDF   |    PREVIOUS   |    NEXT


Title

Future Innovations in Viral Immune Surveillance: A Novel Place for Bioinformation and Artificial Intelligence in the Administration of Health Care

 

Authors

Francesco Chiappelli1,2*, Nicole Balenton1,2,3, Allen Khakshooy1,4

 

Affiliation

1UCLA Center for the Health Sciences, School of Dentistry, Los Angeles, CA;

2CSUN Department of the Health Sciences, Northridge, CA;

3UCLA School of Nursing;

4Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel 3109601;

 

Email

fchiappelli@dentistry.ucla.edu

 

Article Type

Hypothesis

 

Date

Received May 00, 2018; Revised May 00, 2018; Accepted May 00, 2018; Published May 31, 2018

 

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 (Π) 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).

 

Keywords

Bioinformation, artificial intelligence, tweening, multiple regression, viral immune surveillance, Bunyavirus

 

Citation

Chiappelli et al. Bioinformation 14(5): 201-205 (2018)

 

Edited by

P Kangueane

 

ISSN

0973-2063

 

Publisher

Biomedical Informatics

 

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.