Jing Liao

The Automated Systematic Search Deduplicator (ASySD): a rapid, open-source, interoperable tool to remove duplicate citations in biomedical systematic reviews.

ASySD is a tool that efficiently removes duplicate citations in biomedical systematic reviews with high sensitivity and reliability.

Risk of Bias Assessment in Preclinical Literature using Natural Language Processing

This paper applies natural language processing to assess risk of bias in preclinical literature, achieving high F1 scores for several bias items using neural models and BERT.

Automation of citation screening in pre-clinical systematic reviews

The paper develops machine learning classifiers to automate citation screening in pre-clinical systematic reviews, achieving high sensitivity and specificity across different domains.

Animal models of chemotherapy-induced peripheral neuropathy: a machine-assisted systematic review and meta-analysis A comprehensive summary of the field to inform robust experimental design

Machine learning algorithms for systematic reviews: reducing workload in a preclinical review of animal studies and reducing human screening error

This paper applies machine learning to citation screening in systematic reviews of animal studies, improving sensitivity and identifying human errors.

Estimating the statistical performance of different approaches to meta-analysis of data from animal studies in identifying the impact of aspects of study design

This paper compares statistical methods for meta-analysis of animal studies, finding that normalized mean difference (NMD) has higher power than standardized mean difference (SMD), and meta-regression outperforms stratified meta-analysis.