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Permafrost viremia and immune tweening
Jaden Penhaskashi1, Olivia Sekimoto2, Francesco Chiappelli3,4
1Division of West Valley Dental Implant Center, Encino, CA 91316, USA.
Bioinformation
|October 27, 2023
Summary
Deep learning models can enhance our understanding of the immune system. These artificial intelligence tools help predict immune responses to viruses, including ancient ones from thawing permafrost.
Area of Science:
- Immunology
- Computational Biology
- Virology
Background:
- The immune system uses diverse cells and factors for surveillance, distinguishing self from non-self antigens.
- Pathologies arise from dysregulated inflammation (cytokine storm) or autoimmunity.
- Major Histocompatibility Complexes (MHC) present peptides for immune recognition.
Purpose of the Study:
- To explore the role of Deep Learning (DL) in understanding immunological surveillance.
- To investigate DL's ability to predict immune responses to viral threats, including ancient viruses.
Main Methods:
- Application of Deep Learning (DL), a machine learning paradigm, to analyze complex immunological data.
- Utilizing AI algorithms to interpret immune parameters, pathways, and events.
- Analyzing large datasets to draw predictive inferences on immune reactivity.
Main Results:
- DL models offer powerful algorithms for predicting immune responses and homeostasis.
- AI can interpret complex immune data, aiding in understanding immune allostasis.
- DL shows potential in characterizing immune surveillance against novel and ancient viruses.
Conclusions:
- Deep Learning models are crucial for advancing the characterization of immunological surveillance.
- AI can significantly improve the prediction of immune responses to diverse viral challenges.
- Understanding immune responses to ancient viruses from thawing permafrost is a key application for DL.
Keywords:
Autologous Immune Enhancement TherapyB cell immunityCD25 (α-chain of the interleukin[IL]2 receptor, marker of T cell activation)CD279 (programmed cell death marker-1)CD45R0 (marker of immune cell memory differentiation: ultimate restriction fragment [0] of the common leukocyte antigenCD45RA (marker of naive CD4 and CD8 T cellsCD62L (l-selectin, marker of T cells migration)Deep Ensemble LearningDeep LearningGenerative Adversarial NetworksGlycA (glycoprotein acetylation, systemic biomarker of systemic inflammation and autoimmunity)Immune tweeningInflammationMachine LearningMolecular Dynamics SimulationNeuronal NetworksPermafrost pathogensT cell immunityTRegs (CD4/8+CD45RA+/R0+FoxP3+)TcR[αβ](T cell receptor endowed with the α and β chains)Tim-3 (T cell immunoglobulin and mucin domain 3)Tracking Responders EXpandingViremiacluster of differentiation [CD]45)first restriction fragment [A] of CD45)
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