Abstract

Datamining without an age filter proves challenging, especially when searching for direct data from human infants. Although online databases provide immune interaction networks, they often lack information about the age of data sources, resulting in categorizations as age-unspecified. This limitation underrepresents the physiology of naïve immune systems in infants, particularly since full-term infant immunity transitions to an adult-like phenotype between 24 and 30 months of age. This study aims to reconstruct an age-specific immune interaction network for full-term infants by integrating literature-based evidence with existing online database content. A list of 60 extracellular protein candidates involved in immune responses was compiled and refined based on data availability across research articles, two online databases, and cross-referenced validation. From this, 20 proteins were selected based on the strength and abundance of age-specific evidence. Search parameters were refined to evaluate both source and target interactions. Infants were categorized as pre-term or full-term, with the majority of data corresponding to the latter. The interaction networks were constructed using node (cell/protein) and edge (interaction) tables. Edge tables were organized into cell–protein and protein–protein interaction types. Every possible pairwise combination among the 20 proteins was systematically searched in the literature and carefully evaluated to confirm correct age grouping, experimental methods, and direct evidence of interaction. This study identified notable inconsistencies between protein interaction pathways presented in online databases and those reported directly in the literature. Specifically, 18 cell–protein interactions and 9 protein–protein interactions listed in age-unspecified databases were not found in studies specific to full-term infants. In contrast, literature-based findings substantially enriched the reconstructed networks: 82 of 95 cell–protein interactions and 79 of 99 protein–protein interactions were newly added, enhancing the comprehensiveness of curated data on early immune development.

Library of Congress Subject Headings

Molecular immunology; Infants--Health and hygiene; Protein-protein interactions; Data mining

Publication Date

5-2025

Document Type

Thesis

Student Type

Graduate

Degree Name

Bioinformatics (MS)

Advisor

None provided

Campus

RIT – Main Campus

Plan Codes

BIOINFO-MS

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