ProbiEase: AI Platform of
Probiotia Design for Diseases
Translating scattered experimental traits, functional evidence and application descriptions into a machine-computable, semantically aligned structure is foundational for precision probiotic design and clinical translation.
ProbiEase assembles multi-dimensional strain knowledge (taxonomy, phenotypes, tolerance, interactions, function, evidence, application state, etc.) into a unified semantic graph enabling natural‑language querying and reasoning. “AI-PPDD” (AI Platform for Probiotic Design for Diseases) remains only an explanatory functional label.
The sections below outline: background, data scope & structure, and concrete value across research, industry and clinical decision support.
Project Background
Probiotics influence immune modulation, gut homeostasis and multi‑system disease intervention, yet source data is highly fragmented: papers, patents, reports and production notes lack a unified structure—manual comparison is slow and error‑prone. A trustworthy (structured + traceable) and efficient (semantic direct access) knowledge entry point is missing.
ProbiEase centers on “structural standardization + knowledge graph + semantic retrieval + RAG reasoning”. A unified ID system and curated field schema support the full path from rapid lookup → deep comparison → evidence trace-back. AI-PPDD (AI Platform for Probiotic Design for Diseases) is merely the English abbreviation describing this capability paradigm.
Database Scale & Content
Currently: 27 genera · 108 species · 211 subspecies mapped to 241 disease / condition associations (continuously expanding). A lightweight Excel → structured ingestion → API pipeline eases collaboration and future DB migration. Field coverage includes:
• Identification: scientific name / unified ID / taxonomy • Morphology & Growth: Gram stain, colony traits, growth cycle • Cultivation: media, conditions, cost factors • Tolerance & Survival: acid / bile resistance, in vivo persistence • Metabolism & Nutrition: metabolic profile, prebiotic synergy • Interactions & Function: synergy / inhibition, host immune modulation • Genomics & Stability: key expression / genetic stability • Application & Safety: dosage guidance, development phase, risk notes • Evidence: linked literature / patents / evidence level
Standardization ensures scientific granularity while preserving practical applicability (safety, operability, differentiating attributes).
Significance & Value
Research – Unified aggregation + semantic comparison shortens literature synthesis and hypothesis generation; highlights differential or synergistic attributes.
Industry (R&D / Product) – Targeted filtering of candidate strains with specific tolerance, interaction or functional evidence accelerates formulation design and evaluation while reducing trial‑and‑error costs.
Clinical & Translation – Structured safety and evidence elements aid evidence‑based consideration (e.g., indication linkage, dosage ranges, evidence levels).
Semantic retrieval + RAG elevate the platform beyond a static repository. Example queries: "Which strains have human studies supporting IBS improvement?" or "List strains in industrial‑scale production with high bile resistance"—the system returns structured answers with traceable sources.