<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<ArticleSet>
<Article>
<Journal>
<PublisherName>International Academy of Ecology and Environmental Sciences</PublisherName>
<JournalTitle>Network Biology</JournalTitle>
<eissn>2220-8879</eissn>
<Volume>16</Volume>
<Issue>2</Issue>
<PubDate PubStatus="ppublish">
<Year>2026</Year>
<Month>6</Month>
<Day>1</Day>
</PubDate>
</Journal>
<ArticleTitle>TraitGenePathAna: The AI-Powered biological trait analysis platform</ArticleTitle>
<Pages>49-81</Pages>
<Language>EN</Language>
<AuthorList>
<Author>WenJun Zhang</Author>
</AuthorList>
<ArticleList>
<ArticleId IdType="url">http://www.iaees.org/publications/journals/nb/articles/2026-16(2)/TraitGenePathAna.pdf</ArticleId>>
</ArticleList>
<Abstract>
The AI-Powered biological trait analysis platform, TraitGenePathAna, is a single-page web application that helps a user explore the biology behind a trait (e.g., longevity, disease resistance) for a chosen species (e.g., Homo sapiens, Drosophila melanogaster). It does this by sending a structured prompt to an LLM provider (DeepSeek or Google Gemini, etc.) and then presenting the model's response in a multi-tab results UI: (1) Overview: summary, significance, broad context; (2) Genetics: key genes, loci, heritability and gene-level discussion; (3) Pathways: molecular mechanisms, signaling cascades, network view; (4) Interventions: potential strategies and caveats (research, ethics, feasibility). In addition, the platform can conduct inferences on hidden rules, patterns, and relationships.
</Abstract>
</Article>
</ArticleSet>
