Advancing AI for Great Good
Agentic AI inBioinformatics
Pioneering the future of AI-driven biomedical research with cutting-edge agentic systems for discovering knowledge, accelerating drug discovery, and transforming healthcare.
Who We Are
Our Mission
HaoAI is dedicated to developing agentic AI systems that transform biomedical research and healthcare for the greater good of humanity.
Our organization focuses on cutting-edge research at the intersection of artificial intelligence and bioinformatics. We believe that agentic AI systems—AI agents that can autonomously reason, act, and collaborate—hold tremendous potential for accelerating scientific discovery, improving patient outcomes, and addressing some of the most pressing challenges in healthcare and life sciences.
Innovation
Pushing the boundaries of AI research to solve complex biomedical challenges
Collaboration
Working with researchers worldwide to accelerate scientific discovery
Ethics
Committed to responsible AI development with strong ethical principles
What We Work On
Research Focus Areas
Our research spans multiple disciplines at the intersection of AI and biomedical sciences, with a focus on developing agentic systems that can autonomously solve complex problems.
Drug Discovery
Using agentic AI to identify novel therapeutic targets, predict drug interactions, and accelerate the development of new treatments.
Clinical Trials
Developing AI agents for patient matching, eligibility screening, and real-time monitoring of clinical trial data to enhance research efficiency.
Genomics
Applying machine learning and agentic AI to analyze genomic data, identify disease markers, and advance personalized medicine approaches.
Knowledge Graphs
Building and querying biomedical knowledge graphs using AI agents to uncover hidden relationships and accelerate scientific discovery.
EHR Analysis
Leveraging AI agents to extract insights from electronic health records, predict patient outcomes, and improve clinical decision-making.
NLP in Healthcare
Using natural language processing to extract information from clinical literature, medical reports, and biomedical text data.
Current Initiatives
Our Projects
Explore our ongoing research projects that push the boundaries of AI applications in bioinformatics and healthcare.
CTCR Skills
Domain knowledge and skills for Clinical Trial Cohort Research - Agent skills, MCP patterns, and pipeline templates for building clinical trial eligibility assessment pipelines.
Ontology-based Clinical Study Categorization
Data, source code, and result for the Journal of Biomedical Informatics paper using SNOMED CT and MeSH ontologies to categorize clinical studies by their conditions.
Race-sensitive Embeddings for MIMIC-IV
Research on whether race-sensitive biomedical embeddings improve healthcare predictive models using MIMIC-IV data. Trained Word2vec and BERT embeddings for hospital stay and ICU readmission prediction.
Race and Ethnicity Extraction
Automatically extract race and ethnicity from free text using Python regex patterns, supporting clinical study demographic analysis.
Evidence Map Visualization
Evidence map repository for visualizing clinical study evidence and relationships, built with Vue.js frontend.
Clinical Trial NER
Named Entity Recognition (NER) for clinical trials, extracting structured information from clinical trial text documents.
Our Research
Selected Publications
Explore our peer-reviewed research publications in top venues focusing on AI applications in bioinformatics and healthcare.
Retrieval Augmented Scientific Claim Verification
Hao Liu, Soroush Ali, Jordan G. Nestor, Elizabeth Park, Betina Idnay, Yilu Fang, et al.
Evaluating the veracity of PICO-based claims against clinical trial literature on PubMed using CoVERt dataset and CliVER system.
Can Race-sensitive Biomedical Embeddings Improve Healthcare Predictive Models?
Hao Liu, Nour Moustafa-Fahmy, Casey Ta, Chunhua Weng
Algorithm weighing race distribution data in biomedical embeddings for improved healthcare prediction models.
Ontology-based Categorization of Clinical Studies by Their Conditions
Hao Liu, Simona Carini, Zhehuan Chen, Spencer Phillips Hey, Ida Sim, Chunhua Weng
Method for automated ontology-based categorization of clinical studies using SNOMED CT concepts.
A Knowledge Base of Clinical Trial Eligibility Criteria
Hao Liu, Chi Yuan, Alex Butler, Yingcheng Sun, Chunhua Weng
Comprehensive knowledge base of discrete clinical trial eligibility criteria with web-based user interface.
Concept Placement using BERT Trained by Transforming and Summarizing Biomedical Ontology Structure
Hao Liu, Yehoshua Perl, James Geller
Method to automatically predict IS-A relationships between concepts in ontologies using BERT language representation model.
People Behind HaoAI
Our Team
Meet our dedicated team of researchers and contributors working to advance agentic AI for the greater good.
Dr. Hao Liu
Founder & Principal Investigator
Assistant Professor in the School of Computing at Montclair State University and Founder of HaoAI. Research focuses on developing agentic AI systems for healthcare applications with emphasis on clinical decision support, biomedical ontology engineering, and natural language processing for clinical text.