Journal Article

VAIV bio-discovery service using transformer model and retrieval augmented generation

Kim, Seonho, Yoon, Juntae

2024

Overview

Background There has been a considerable advancement in AI technologies like LLM and machine learning to support biomedical knowledge discovery. Main body We propose a novel biomedical neural search service called ‘VAIV Bio-Discovery’, which supports enhanced knowledge discovery and document search on unstructured text such as PubMed. It mainly handles with information related to chemical compound/drugs, gene/proteins, diseases, and their interactions (chemical compounds/drugs-proteins/gene including drugs-targets, drug-drug, and drug-disease). To provide comprehensive knowledge, the system offers four search options: basic search, entity and interaction search, and natural language search. We employ T5slim_dec, which adapts the autoregressive generation task of the T5 (text-to-text transfer transformer) to the interaction extraction task by removing the self-attention layer in the decoder block. It also assists in interpreting research findings by summarizing the retrieved search results for a given natural language query with Retrieval Augmented Generation (RAG). The search engine is built with a hybrid method that combines neural search with the probabilistic search, BM25. Conclusion As a result, our system can better understand the context, semantics and relationships between terms within the document, enhancing search accuracy. This research contributes to the rapidly evolving biomedical field by introducing a new service to access and discover relevant knowledge.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Algorithms / Attention task / Big data management in biological domains / Bioinformatics / Biomedical and Life Sciences / Biomedical interaction extraction / Chatbots / Chemical compounds / Computational Biology/Bioinformatics / Computational linguistics / Computer Appl. in Life Sciences / Data mining / Data Mining - methods / Deep learning / Documents / Drug discovery / Drug interaction / Drugs / Electric transformers / Engine blocks / Genes / Information Storage and Retrieval - methods / Keywords / Knowledge discovery / Knowledge Discovery - methods / Language / Language processing / Large language models / Life Sciences / LLM / Machine Learning / Medical research / Medical Subject Headings-MeSH / Medicine, Experimental / Methods / Microarrays / Natural language / Natural language interfaces / Natural Language Processing / Neural Networks, Computer / Proteins / PubMed / Queries / Query languages / RAG / Retrieval augmented generation / Search Engine / Search engines / Semantics / Speech recognition / Technology application / Text mining / Transformer / Transformers / Trends / Unstructured data