Engineering topics

Zou, Wen

Publications and source records attributed to Zou, Wen.

3 records

DOE OSTI2025

AI-powered topic modeling: comparing LDA and BERTopic in analyzing opioid-related cardiovascular risks in women

Topic modeling is a crucial technique in natural language processing (NLP), enabling the extraction of latent themes from large text corpora. Traditional topic modeling, such as Latent Dirichlet Allocation (LDA), faces limitations in capturing the semantic relationships in the text document although it has been widely applied in text mining. BERTopic, created in 2022, leveraged advances in deep learning and can capture the contextual relationships between words. In this work, we integrated Artificial Intelligence (AI) modules to LDA and BERTopic and provided a comprehensive comparison on the analysis of prescription opioid-related cardiovascular risks in women. Opioid use can increase the risk of cardiovascular problems in women such as arrhythmia, hypotension etc. 1,837 abstracts were retrieved and downloaded from PubMed as of April 2024 using three Medical Subject Headings (MeSH) words: “opioid,” “cardiovascular,” and “women.” Machine Learning of Language Toolkit (MALLET) was employed for the implementation of LDA. BioBERT was used for document embedding in BERTopic. Eighteen was selected as the optimal topic number for MALLET and 23 for BERTopic. ChatGPT-4-Turbo was integrated to interpret and compare the results. The short descriptions created by ChatGPT for each topic from LDA and BERTopic were highly correlated, and the performance accuracies of LDA and BERTopic were similar as determined by expert manual reviews of the abstracts grouped by their predominant topics. The results of the t-SNE (t-distributed Stochastic Neighbor Embedding) plots showed that the clusters created from BERTopic were more compact and well-separated, representing improved coherence and distinctiveness between the topics. Our findings indicated that AI algorithms could augment both traditional and contemporary topic modeling techniques. In addition, BERTopic has the connection port for ChatGPT-4-Turbo or other large language models in its algorithm for automatic interpretation, while with LDA interpretation must be manually, and needs special procedures for data pre-processing and stop words exclusion. Therefore, while LDA remains valuable for large-scale text analysis with resource constraints, AI-assisted BERTopic offers significant advantages in providing the enhanced interpretability and the improved semantic coherence for extracting valuable insights from textual data.

Research & Experimental Medicine↗

DOE OSTI2017

Mechanistic roles of microRNAs in hepatocarcinogenesis: A study of thioacetamide with multiple doses and time-points of rats

Environmental chemicals exposure is one of the primary factors for liver toxicity and hepatocarcinoma. Thioacetamide (TAA) is a well-known hepatotoxicant and could be a liver carcinogen in humans. The discovery of early and sensitive microRNA (miRNA) biomarkers in liver injury and tumor progression could improve cancer diagnosis, prognosis, and management. To study this, we performed next generation sequencing of the livers of Sprague-Dawley rats treated with TAA at three doses (4.5, 15 and 45 mg/kg) and four time points (3-, 7-, 14- and 28-days). Overall, 330 unique differentially expressed miRNAs (DEMs) were identified in the entire TAA-treatment course. Of these, 129 DEMs were found significantly enriched for the “liver cancer” annotation. These results were further complemented by pathway analysis (Molecular Mechanisms of Cancer, p53-, TGF-β-, MAPK- and Wnt-signaling). Two miRNAs (rno-miR-34a-5p and rno-miR-455-3p) out of 48 overlapping DEMs were identified to be early and sensitive biomarkers for TAA-induced hepatocarcinogenicity. We have shown significant regulatory associations between DEMs and TAA-induced liver carcinogenesis at an earlier stage than histopathological features. Most importantly, miR-34a-5p is the most suitable early and sensitive biomarker for TAA-induced hepatocarcinogenesis due to its consistent elevation during the entire treatment course.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

A novel procedure on next generation sequencing data analysis using text mining algorithm

Background: Next-generation sequencing (NGS) technologies have provided researchers with vast possibilities in various biological and biomedical research areas. Efficient data mining strategies are in high demand for large scale comparative and evolutional studies to be performed on the large amounts of data derived from NGS projects. Topic modeling is an active research field in machine learning and has been mainly used as an analytical tool to structure large textual corpora for data mining. Methods: We report a novel procedure to analyse NGS data using topic modeling. It consists of four major procedures: NGS data retrieval, preprocessing, topic modeling, and data mining using Latent Dirichlet Allocation (LDA) topic outputs. The NGS data set of the Salmonella enterica strains were used as a case study to show the workflow of this procedure. The perplexity measurement of the topic numbers and the convergence efficiencies of Gibbs sampling were calculated and discussed for achieving the best result from the proposed procedure. Results: The output topics by LDA algorithms could be treated as features of Salmonella strains to accurately describe the genetic diversity of fliC gene in various serotypes. The results of a two-way hierarchical clustering and data matrix analysis on LDA-derived matrices successfully classified Salmonella serotypes based on the NGS data. The implementation of topic modeling in NGS data analysis procedure provides a new way to elucidate genetic information from NGS data, and identify the gene-phenotype relationships and biomarkers, especially in the era of biological and medical big data. Conclusion: The implementation of topic modeling in NGS data analysis provides a new way to elucidate genetic information from NGS data, and identify the gene-phenotype relationships and biomarkers, especially in the era of biological and medical big data.

59 BASIC BIOLOGICAL SCIENCES↗