arXiv subjects

Lanjing Zhang

Publications and source records attributed to Lanjing Zhang.

4 recordsLinked to original sources

arXiv2026

Errors in AI-Assisted Retrieval of Medical Literature: A Comparative Study

Large language models (LLMs) assisted literature retrieval may lead to erroneous references, but these errors have not been rigorously quantified. Therefore, we quantitatively assess errors in reference retrieval of widely used free-version LLM platforms and identify the factors associated with retrieval errors. We evaluated 2,000 references retrieved by 5 LLMs (Grok-2, ChatGPT GPT-4.1, Google Gemini Flash 2.5, Perplexity AI, and DeepSeek GPT-4) for 40 randomly-selected original articles (10 per journal) published Jan. 2024 to July 2025 from British Medical Journal (BMJ), Journal of the American Medical Association, and The New England Journal of Medicine (NEJM). Primary outcomes were a multimetric score ratio combining validity of digital object identifier, PubMed ID, Google-Scholar link, and relevance; and complete miss rate (proportion of references failing all applicable metrics). Multivariable regression was used to examine independent associations. LLM platforms completely failed to retrieve correct reference data 47.8% of the time. The average score ratio of the 5 LLM platforms was 0.29 (standard deviation, 0.35; range, 0-1.25), with a higher score ratio indicating a higher accuracy in retrieving relevant references and correct bibliographic data. The highest and lowest accuracies were achieved by Grok (0.57) and Genimi (0.11), respectively. Compared with BMJ, NEJM articles had lower score ratios and higher complete miss rates. Multivariable analysis shows LLM platforms and journals were independently associated with score ratios and complete miss rate, respectively. We show modest overall performance of LLMs and significant variability in retrieval accuracy across platforms and journals. LLM platforms and journals are associated with LLM's performance in retrieving medical literature. Bibliographic data should be carefully reviewed when using LLM-assisted literature retrieval.

cs.IR↗

arXiv2025

Association of normalization, non-differentially expressed genes and data source with machine learning performance in intra-dataset or cross-dataset modelling of transcriptomic and clinical data

Cross-dataset testing is critical for examining machine learning (ML) model's performance. However, most studies on modelling transcriptomic and clinical data only conducted intra-dataset testing. It is also unclear whether normalization and non-differentially expressed genes (NDEG) can improve cross-dataset modeling performance of ML. We thus aim to understand whether normalization, NDEG and data source are associated with performance of ML in cross-dataset testing. The transcriptomic and clinical data shared by the lung adenocarcinoma cases in TCGA and ONCOSG were used. The best cross-dataset ML performance was reached using transcriptomic data alone and statistically better than those using transcriptomic and clinical data. The best balance accuracy, area under curve and accuracy were significantly better in ML algorithms training on TCGA and tested on ONCOSG than those trained on ONCOSG and tested on TCGA (p<0.05 for all). Normalization and NDEG greatly improved intra-dataset ML performances in both datasets, but not in cross-dataset testing. Strikingly, modelling transcriptomic data of ONCOSG alone outperformed modelling transcriptomic and clinical data whereas including clinical data in TCGA did not significantly impact ML performance, suggesting limited clinical data value or an overwhelming influence of transcriptomic data in TCGA. Performance gains in intra-dataset testing were more pronounced for ML models trained on ONCOSG than TCGA. Among the six ML models compared, Support vector machine was the most frequent best-performer in both intra-dataset and cross-dataset testing. Therefore, our data show data source, normalization and NDEG are associated with intra-dataset and cross-dataset ML performance in modelling transcriptomic and clinical data.

q-bio.QM↗

arXiv2025

Normalization and selecting non-differentially expressed genes improve machine learning modelling of cross-platform transcriptomic data

Normalization is a critical step in quantitative analyses of biological processes. Recent works show that cross-platform integration and normalization enable machine learning (ML) training on RNA microarray and RNA-seq data, but no independent datasets were used in their studies. Therefore, it is unclear how to improve ML modelling performance on independent RNA array and RNA-seq based datasets. Inspired by the house-keeping genes that are commonly used in experimental biology, this study tests the hypothesis that non-differentially expressed genes (NDEG) may improve normalization of transcriptomic data and subsequently cross-platform modelling performance of ML models. Microarray and RNA-seq datasets of the TCGA breast cancer were used as independent training and test datasets, respectively, to classify the molecular subtypes of breast cancer. NDEG (p>0.85) and differentially expressed genes (DEG, p<0.05) were selected based on the p values of ANOVA analysis and used for subsequent data normalization and classification, respectively. Models trained based on data from one platform were used for testing on the other platform. Our data show that NDEG and DEG gene selection could effectively improve the model classification performance. Normalization methods based on parametric statistical analysis were inferior to those based on nonparametric statistics. In this study, the LOG_QN and LOG_QNZ normalization methods combined with the neural network classification model seem to achieve better performance. Therefore, NDEG-based normalization appears useful for cross-platform testing on completely independent datasets. However, more studies are required to examine whether NDEG-based normalization can improve ML classification performance in other datasets and other omic data types.

q-bio.QM↗

arXiv2024

Fairness of ChatGPT

Understanding and addressing unfairness in LLMs are crucial for responsible AI deployment. However, there is a limited number of quantitative analyses and in-depth studies regarding fairness evaluations in LLMs, especially when applying LLMs to high-stakes fields. This work aims to fill this gap by providing a systematic evaluation of the effectiveness and fairness of LLMs using ChatGPT as a study case. We focus on assessing ChatGPT's performance in high-takes fields including education, criminology, finance and healthcare. To conduct a thorough evaluation, we consider both group fairness and individual fairness metrics. We also observe the disparities in ChatGPT's outputs under a set of biased or unbiased prompts. This work contributes to a deeper understanding of LLMs' fairness performance, facilitates bias mitigation and fosters the development of responsible AI systems.

cs.LG↗