Liang-Chin Huang

Liang-Chin Huang

University of Georgia

H-index: 10

North America-United States

About Liang-Chin Huang

Liang-Chin Huang, With an exceptional h-index of 10 and a recent h-index of 10 (since 2020), a distinguished researcher at University of Georgia, specializes in the field of Bioinformatics, Machine Learning, Pharmacogenomics, Natural Language Processing, Knowledge Graph.

His recent articles reflect a diverse array of research interests and contributions to the field:

Ensemble pretrained language models to extract biomedical knowledge from literature

AutoCriteria: a generalizable clinical trial eligibility criteria extraction system powered by large language models

Extracting Drug-Protein Relation from Literature Using Ensembles of Biomedical Transformers

Granularly, Precisely, and Timely: Leveraging Large Language Models for Safety and Efficacy Extraction in Oncology Clinical Trial Abstracts (SEETrials)

Dark kinase annotation, mining, and visualization using the Protein Kinase Ontology

MSR126 AutoCriteria: Advancing Clinical Trial Study With AI-Powered Eligibility Criteria Extraction

Predicting protein and pathway associations for understudied dark kinases using pattern-constrained knowledge graph embedding

RWD108 Vaccine Sentiments on Social Media: A Machine Learning-Powered Real-Time Monitoring System

Liang-Chin Huang Information

University

Position

Institute of Bioinformatics

Citations(all)

621

Citations(since 2020)

392

Cited By

385

hIndex(all)

10

hIndex(since 2020)

10

i10Index(all)

12

i10Index(since 2020)

11

Email

University Profile Page

Google Scholar

Liang-Chin Huang Skills & Research Interests

Bioinformatics

Machine Learning

Pharmacogenomics

Natural Language Processing

Knowledge Graph

Top articles of Liang-Chin Huang

Ensemble pretrained language models to extract biomedical knowledge from literature

Journal of the American Medical Informatics Association

2024/3/23

AutoCriteria: a generalizable clinical trial eligibility criteria extraction system powered by large language models

Journal of the American Medical Informatics Association

2024/2/1

Extracting Drug-Protein Relation from Literature Using Ensembles of Biomedical Transformers

2024

Granularly, Precisely, and Timely: Leveraging Large Language Models for Safety and Efficacy Extraction in Oncology Clinical Trial Abstracts (SEETrials)

medRxiv

2024

Dark kinase annotation, mining, and visualization using the Protein Kinase Ontology

PeerJ

2023/12/5

MSR126 AutoCriteria: Advancing Clinical Trial Study With AI-Powered Eligibility Criteria Extraction

Value in Health

2023/12/1

Predicting protein and pathway associations for understudied dark kinases using pattern-constrained knowledge graph embedding

PeerJ

2023/10/18

RWD108 Vaccine Sentiments on Social Media: A Machine Learning-Powered Real-Time Monitoring System

Value in Health

2023/6/1

MSR78 Knowledgesphere: An Automated and Integrative Framework for Drug Repurposing Empowered By Knowledge Graph and AI

Value in Health

2023/6/1

Mutations in protein kinase Cγ promote spinocerebellar ataxia type 14 by impairing kinase autoinhibition

Science signaling

2022/9/27

Liang-Chin Huang
Liang-Chin Huang

H-Index: 8

Natarajan Kannan
Natarajan Kannan

H-Index: 28

KinOrtho: a method for mapping human kinase orthologs across the tree of life and illuminating understudied kinases

BMC bioinformatics

2021/12

UTHealth@ BioCreativeVII: domain-specific transformer models for drug-protein relation extraction

2021

Quantitative Structure–Mutation–Activity Relationship Tests (QSMART) model for protein kinase inhibitor response prediction

BMC bioinformatics

2020/12

A resource for exploring the understudied human kinome for research and therapeutic opportunities

BioRxiv

2020/4/2

Deep evolutionary analysis reveals the design principles of fold A glycosyltransferases

elife

2020/4/1

See List of Professors in Liang-Chin Huang University(University of Georgia)