We are committed

to mining, curating and inferring

Biomedical Literature Knowledge

The Lit-Evi site is for showcasing all the main projects and developed/ing resources in the HZAU BioNLP lab. The resources mainly relate to literature evidence mined and curated by BioNLP methods and inferred knowledge by Bayesian frameworks.

Main Projects

AGAC Corpus

“Annotation of Genes with Alteration-Centric function changes”. — A customized corpus for mining functions caused by mutations.


Literature-mined resources for regulatory events caused by genetic alterations. The extraction and curation of the event items is based on the in-house developed pipeline, GARE.



This database includes 16,681 thousand of regulatory events records encompassing 21K genes, 157K genetic alterations and 154K downstream bio-concepts, extracted from 4,354K pan-cancer literature.


Alzheimer's Disease

A comprehensive database supporting the precise interpretation of the underlying pathological mechanisms of Alzheimer's disease. The web service provide convenient data queries, evidence displays, and result visualizations.



An ongoing project for rice annotation.

Ontology Development

We are building ontologies for concept normalization.

Rice Trait Ontology


The rice trait ontology (RTO) project is to define rice-specific ontology concepts for rice traits and phenotypes.

Knowledge Visualization

Various research strategies are used to extract, curate, and infer the knowledge bio-medicine. The main methods include BioNLP pipeline, Bayesian algorithms, and web-based data service.


An evidence-augmented data fusion is run to support the driver gene discovery via interpretable literature evidence and omics data. Case studies in AD, lung cancer and breast cancer.


A web service for querying the pathology networks with rich literature evidence in the Alzheimer's Disease context.


An ongoing project for triplet evaluation in the context of Lacidipiscis.


A "creepy" and symbolic version of BLCA event visualization.


AD-LitMiner aims to present the most valuable AD literature statements and provides drug recommendations.

Selected Publications

Developing Team


Affiliation: College of Informatics, Huazhong Agricultural University, Wuhan, Hubei, China

Lab PI: Jingbo Xia

All the contributors refer to above publications.