Evaluating the OpenAI's GPT-3.5 Turbo's performance in extracting information from scientific articles on diabetic retinopathy.

We aimed to compare the concordance of information extracted and the time taken between a large language model (OpenAI's GPT-3.5 Turbo via API) against conventional human extraction methods in retrieving information from scientific articles on diabetic retinopathy (DR). The extraction was done using GPT3.5 Turbo as of October 2023. OpenAI's GPT-3.5 Turbo significantly reduced the time taken for extraction. Concordance was highest at 100% for the extraction of the country of study, 64.7% for significant risk factors of DR, 47.1% for exclusion and inclusion criteria, and lastly 41.2% for odds ratio (OR) and 95% confidence interval (CI). The concordance levels seemed to indicate the complexity associated with each prompt. This suggests that OpenAI's GPT-3.5 Turbo may be adopted to extract simple information that is easily located in the text, leaving more complex information to be extracted by the researcher. It is crucial to note that the foundation model is constantly improving significantly with new versions being released quickly. Subsequent work can focus on retrieval-augmented generation (RAG), embedding, chunking PDF into useful sections, and prompting to improve the accuracy of extraction.

Mining Real-World Big Data to Characterize Adverse Drug Reaction Quantitatively: Mixed Methods Study.

BACKGROUND: Adverse drug reactions (ADRs), which are the phenotypic manifestations of clinical drug toxicity in humans, are a major concern in precision clinical medicine. A comprehensive evaluation of ADRs is helpful for unbiased supervision of marketed drugs and for discovering new drugs with high success rates. OBJECTIVE: In current practice, drug safety evaluation is often oversimplified to the occurrence or nonoccurrence of ADRs. Given the limitations of current qualitative methods, there is an urgent need for a quantitative evaluation model to improve pharmacovigilance and the accurate assessment of drug safety. METHODS: In this study, we developed a mathematical model, namely the Adverse Drug Reaction Classification System (ADReCS) severity-grading model, for the quantitative characterization of ADR severity, a crucial feature for evaluating the impact of ADRs on human health. The model was constructed by mining millions of real-world historical adverse drug event reports. A new parameter called Severity_score was introduced to measure the severity of ADRs, and upper and lower score boundaries were determined for 5 severity grades. RESULTS: The ADReCS severity-grading model exhibited excellent consistency (99.22%) with the expert-grading system, the Common Terminology Criteria for Adverse Events. Hence, we graded the severity of 6277 standard ADRs for 129,407 drug-ADR pairs. Moreover, we calculated the occurrence rates of 6272 distinct ADRs for 127,763 drug-ADR pairs in large patient populations by mining real-world medication prescriptions. With the quantitative features, we demonstrated example applications in systematically elucidating ADR mechanisms and thereby discovered a list of drugs with improper dosages. CONCLUSIONS: In summary, this study represents the first comprehensive determination of both ADR severity grades and ADR frequencies. This endeavor establishes a strong foundation for future artificial intelligence applications in discovering new drugs with high efficacy and low toxicity. It also heralds a paradigm shift in clinical toxicity research, moving from qualitative description to quantitative evaluation.

Stacking with Recursive Feature Elimination-Isolation Forest for classification of diabetes mellitus.

Diabetes Mellitus is one of the oldest diseases known to humankind, dating back to ancient Egypt. The disease is a chronic metabolic disorder that heavily burdens healthcare providers worldwide due to the steady increment of patients yearly. Worryingly, diabetes affects not only the aging population but also children. It is prevalent to control this problem, as diabetes can lead to many health complications. As evolution happens, humankind starts integrating computer technology with the healthcare system. The utilization of artificial intelligence assists healthcare to be more efficient in diagnosing diabetes patients, better healthcare delivery, and more patient eccentric. Among the advanced data mining techniques in artificial intelligence, stacking is among the most prominent methods applied in the diabetes domain. Hence, this study opts to investigate the potential of stacking ensembles. The aim of this study is to reduce the high complexity inherent in stacking, as this problem contributes to longer training time and reduces the outliers in the diabetes data to improve the classification performance. In addressing this concern, a novel machine learning method called the Stacking Recursive Feature Elimination-Isolation Forest was introduced for diabetes prediction. The application of stacking with Recursive Feature Elimination is to design an efficient model for diabetes diagnosis while using fewer features as resources. This method also incorporates the utilization of Isolation Forest as an outlier removal method. The study uses accuracy, precision, recall, F1 measure, training time, and standard deviation metrics to identify the classification performances. The proposed method acquired an accuracy of 79.077% for PIMA Indians Diabetes and 97.446% for the Diabetes Prediction dataset, outperforming many existing methods and demonstrating effectiveness in the diabetes domain.

Paving the way for COVID survivors' psychosocial rehabilitation: Mining topics, sentiments, and their trajectories over time from Reddit.

Objective: This study examined major themes and sentiments and their trajectories and interactions over time using subcategories of Reddit data. The aim was to facilitate decision-making for psychosocial rehabilitation. Materials and Methods: We utilized natural language processing techniques, including topic modeling and sentiment analysis, on a dataset consisting of more than 38,000 topics, comments, and posts collected from a subreddit dedicated to the experiences of people who tested positive for COVID-19. In this longitudinal exploratory analysis, we studied the dynamics between the most dominant topics and subjects' emotional states over an 18-month period. Results: Our findings highlight the evolution of the textual and sentimental status of major topics discussed by COVID survivors over an extended period of time during the pandemic. We particularly studied pre- and post-vaccination eras as a turning point in the timeline of the pandemic. The results show that not only does the relevance of topics change over time, but the emotions attached to them also vary. Major social events, such as the administration of vaccines or enforcement of nationwide policies, are also reflected through the discussions and inquiries of social media users. In particular, the emotional state (i.e., sentiments and polarity of their feelings) of those who have experienced COVID personally. Discussion: Cumulative societal knowledge regarding the COVID-19 pandemic impacts the patterns with which people discuss their experiences, concerns, and opinions. The subjects' emotional state with respect to different topics was also impacted by extraneous factors and events, such as vaccination. Conclusion: By mining major topics, sentiments, and trajectories demonstrated in COVID-19 survivors' interactions on Reddit, this study contributes to the emerging body of scholarship on COVID-19 survivors' mental health outcomes, providing insights into the design of mental health support and rehabilitation services for COVID-19 survivors.

The Association of Malnutrition and Health-Related Factors among 474,467 Older Community-Dwellers: A Population-Based Data Mining Study in Guangzhou, China.

BACKGROUND: This study aimed to examine the prevalence and associated factors of malnutrition in older community-dwellers and explore the interaction between associated factors. METHODS: A total of 474,467 older community-dwellers aged 65 or above were selected in Guangzhou, China. We used a two-step methodology to detect the associated factors of malnutrition and constructed logistic regression models to explore the influencing factors and interactive effects on three patterns of malnutrition. RESULTS: The prevalence of malnutrition was 22.28%. Older adults with both hypertension and diabetes (RERI = 0.13), both meat or fish diet and hypertension (RERI = 0.79), and both meat or fish diet and diabetes (RERI = 0.81) had positive additive interaction effects on the risk of obesity, whereas those on a vegetarian diet with hypertension (RERI = -0.25) or diabetes (RERI = -0.19) had negative additive interaction effects. Moreover, the interactions of physical activity with a meat or fish diet (RERI = -0.84) or dyslipidemia (RERI = -0.09) could lower the risk of obesity. CONCLUSIONS: Malnutrition was influenced by different health factors, and there were interactions between these influencing factors. Pertinent dietary instruction should be given according to different nutritional status indexes and the prevalence of metabolic diseases to avoid the occurrences of malnutrition among older adults.

Identifying the Effect of Cognitive Motivation with the Method Based on Temporal Association Rule Mining Concept.

Being motivated has positive influences on task performance. However, motivation could result from various motives that affect different parts of the brain. Analyzing the motivation effect from all affected areas requires a high number of EEG electrodes, resulting in high cost, inflexibility, and burden to users. In various real-world applications, only the motivation effect is required for performance evaluation regardless of the motive. Analyzing the relationships between the motivation-affected brain areas associated with the task's performance could limit the required electrodes. This study introduced a method to identify the cognitive motivation effect with a reduced number of EEG electrodes. The temporal association rule mining (TARM) concept was used to analyze the relationships between attention and memorization brain areas under the effect of motivation from the cognitive motivation task. For accuracy improvement, the artificial bee colony (ABC) algorithm was applied with the central limit theorem (CLT) concept to optimize the TARM parameters. From the results, our method can identify the motivation effect with only FCz and P3 electrodes, with 74.5% classification accuracy on average with individual tests.

Mine the past: How to make better risk-based decisions and improve outcomes with historical threat data.

The impact of every crisis has the potential to cascade throughout an organisation's operations, supply chain and market ecosystem. To properly understand and mitigate this ripple of dynamic risk, business continuity, security and risk management leaders need to know where to focus their attention. Looking at historical threat data provides a clearer picture of the risk landscape, helping leaders better anticipate and plan for the future. To date, however, there have been challenges in this process. As the volume of data about critical events continues to grow at an alarming rate, sifting manually through data puts organisations - and business continuity - in jeopardy. This paper discusses the value of historical threat data and innovations in data-mining technology that can unlock the true power of historical data for informed, strategic decision-making and better outcomes during a crisis.

Identification of pattern mining algorithm for rugby league players positional groups separation based on movement patterns.

The application of pattern mining algorithms to extract movement patterns from sports big data can improve training specificity by facilitating a more granular evaluation of movement. Since movement patterns can only occur as consecutive, non-consecutive, or non-sequential, this study aimed to identify the best set of movement patterns for player movement profiling in professional rugby league and quantify the similarity among distinct movement patterns. Three pattern mining algorithms (l-length Closed Contiguous [LCCspm], Longest Common Subsequence [LCS] and AprioriClose) were used to extract patterns to profile elite rugby football league hookers (n = 22 players) and wingers (n = 28 players) match-games movements across 319 matches. Jaccard similarity score was used to quantify the similarity between algorithms' movement patterns and machine learning classification modelling identified the best algorithm's movement patterns to separate playing positions. LCCspm and LCS movement patterns shared a 0.19 Jaccard similarity score. AprioriClose movement patterns shared no significant Jaccard similarity with LCCspm (0.008) and LCS (0.009) patterns. The closed contiguous movement patterns profiled by LCCspm best-separated players into playing positions. Multi-layered Perceptron classification algorithm achieved the highest accuracy of 91.02% and precision, recall and F1 scores of 0.91 respectively. Therefore, we recommend the extraction of closed contiguous (consecutive) over non-consecutive and non-sequential movement patterns for separating groups of players.

Demystifying COVID-19 mortality causes with interpretable data mining.

While COVID-19 becomes periodical, old individuals remain vulnerable to severe disease with high mortality. Although there have been some studies on revealing different risk factors affecting the death of COVID-19 patients, researchers rarely provide a comprehensive analysis to reveal the relationships and interactive effects of the risk factors of COVID-19 mortality, especially in the elderly. Through retrospectively including 1917 COVID-19 patients (102 were dead) admitted to Xiangya Hospital from December 2022 to March 2023, we used the association rule mining method to identify the risk factors leading causes of death among the elderly. Firstly, we used the Affinity Propagation clustering to extract key features from the dataset. Then, we applied the Apriori Algorithm to obtain 6 groups of abnormal feature combinations with significant increments in mortality rate. The results showed a relationship between the number of abnormal feature combinations and mortality rates within different groups. Patients with "C-reactive protein > 8 mg/L", "neutrophils percentage > 75.0 %", "lymphocytes percentage < 20%", and "albumin < 40 g/L" have a 2 × mortality rate than the basic one. When the characteristics of "D-dimer > 0.5 mg/L" and "WBC > 9.5 × 10 9 /L" are continuously included in this foundation, the mortality rate can be increased to 3 × or 4 × . In addition, we also found that liver and kidney diseases significantly affect patient mortality, and the mortality rate can be as high as 100%. These findings can support auxiliary diagnosis and treatment to facilitate early intervention in patients, thereby reducing patient mortality.

Network-based visualisation of frequent sequences.

Frequent sequence pattern mining is an excellent tool to discover patterns in event chains. In complex systems, events from parallel processes are present, often without proper labelling. To identify the groups of events related to the subprocess, frequent sequential pattern mining can be applied. Since most algorithms provide too many frequent sequences that make it difficult to interpret the results, it is necessary to post-process the resulting frequent patterns. The available visualisation techniques do not allow easy access to multiple properties that support a faster and better understanding of the event scenarios. To answer this issue, our work proposes an intuitive and interactive solution to support this task, introducing three novel network-based sequence visualisation methods that can reduce the time of information processing from a cognitive perspective. The proposed visualisation methods offer a more information rich and easily understandable interpretation of sequential pattern mining results compared to the usual text-like outcome of pattern mining algorithms. The first uses the confidence values of the transitions to create a weighted network, while the second enriches the adjacency matrix based on the confidence values with similarities of the transitive nodes. The enriched matrix enables a similarity-based Multidimensional Scaling (MDS) projection of the sequences. The third method uses similarity measurement based on the overlap of the occurrences of the supporting events of the sequences. The applicability of the method is presented in an industrial alarm management problem and in the analysis of clickstreams of a website. The method was fully implemented in Python environment. The results show that the proposed methods are highly applicable for the interactive processing of frequent sequences, supporting the exploration of the inner mechanisms of complex systems.

Data-Driven Exploration of National Health Service Talking Therapies Care Pathways Using Process Mining: Retrospective Cohort Study.

BACKGROUND: The National Health Service (NHS) Talking Therapies program treats people with common mental health problems in England according to "stepped care," in which lower-intensity interventions are offered in the first instance, where clinically appropriate. Limited resources and pressure to achieve service standards mean that program providers are exploring all opportunities to evaluate and improve the flow of patients through their service. Existing research has found variation in clinical performance and stepped care implementation across sites and has identified associations between service delivery and patient outcomes. Process mining offers a data-driven approach to analyzing and evaluating health care processes and systems, enabling comparison of presumed models of service delivery and their actual implementation in practice. The value and utility of applying process mining to NHS Talking Therapies data for the analysis of care pathways have not been studied. OBJECTIVE: A better understanding of systems of service delivery will support improvements and planned program expansion. Therefore, this study aims to demonstrate the value and utility of applying process mining to NHS Talking Therapies care pathways using electronic health records. METHODS: Routine collection of a wide variety of data regarding activity and patient outcomes underpins the Talking Therapies program. In our study, anonymized individual patient referral records from two sites over a 2-year period were analyzed using process mining to visualize the care pathway process by mapping the care pathway and identifying common pathway routes. RESULTS: Process mining enabled the identification and visualization of patient flows directly from routinely collected data. These visualizations illustrated waiting periods and identified potential bottlenecks, such as the wait for higher-intensity cognitive behavioral therapy (CBT) at site 1. Furthermore, we observed that patients discharged from treatment waiting lists appeared to experience longer wait durations than those who started treatment. Process mining allowed analysis of treatment pathways, showing that patients commonly experienced treatment routes that involved either low- or high-intensity interventions alone. Of the most common routes, >5 times as many patients experienced direct access to high-intensity treatment rather than stepped care. Overall, 3.32% (site 1: 1507/45,401) and 4.19% (site 2: 527/12,590) of all patients experienced stepped care. CONCLUSIONS: Our findings demonstrate how process mining can be applied to Talking Therapies care pathways to evaluate pathway performance, explore relationships among performance issues, and highlight systemic issues, such as stepped care being relatively uncommon within a stepped care system. Integration of process mining capability into routine monitoring will enable NHS Talking Therapies service stakeholders to explore such issues from a process perspective. These insights will provide value to services by identifying areas for service improvement, providing evidence for capacity planning decisions, and facilitating better quality analysis into how health systems can affect patient outcomes.

Large language model based framework for automated extraction of genetic interactions from unstructured data.

Extracting biological interactions from published literature helps us understand complex biological systems, accelerate research, and support decision-making in drug or treatment development. Despite efforts to automate the extraction of biological relations using text mining tools and machine learning pipelines, manual curation continues to serve as the gold standard. However, the rapidly increasing volume of literature pertaining to biological relations poses challenges in its manual curation and refinement. These challenges are further compounded because only a small fraction of the published literature is relevant to biological relation extraction, and the embedded sentences of relevant sections have complex structures, which can lead to incorrect inference of relationships. To overcome these challenges, we propose GIX, an automated and robust Gene Interaction Extraction framework, based on pre-trained Large Language models fine-tuned through extensive evaluations on various gene/protein interaction corpora including LLL and RegulonDB. GIX identifies relevant publications with minimal keywords, optimises sentence selection to reduce computational overhead, simplifies sentence structure while preserving meaning, and provides a confidence factor indicating the reliability of extracted relations. GIX's Stage-2 relation extraction method performed well on benchmark protein/gene interaction datasets, assessed using 10-fold cross-validation, surpassing state-of-the-art approaches. We demonstrated that the proposed method, although fully automated, performs as well as manual relation extraction, with enhanced robustness. We also observed GIX's capability to augment existing datasets with new sentences, incorporating newly discovered biological terms and processes. Further, we demonstrated GIX's real-world applicability in inferring E. coli gene circuits.

Discovery of fungal onoceroid triterpenoids through domainless enzyme-targeted global genome mining.

Genomics-guided methodologies have revolutionized the discovery of natural products. However, a major challenge in the field of genome mining is determining how to selectively extract biosynthetic gene clusters (BGCs) for untapped natural products from numerous available genome sequences. In this study, we developed a fungal genome mining tool that extracts BGCs encoding enzymes that lack a detectable protein domain (i.e., domainless enzymes) and are not recognized as biosynthetic proteins by existing bioinformatic tools. We searched for BGCs encoding a homologue of Pyr4-family terpene cyclases, which are representative examples of apparently domainless enzymes, in approximately 2000 fungal genomes and discovered several BGCs with unique features. The subsequent characterization of selected BGCs led to the discovery of fungal onoceroid triterpenoids and unprecedented onoceroid synthases. Furthermore, in addition to the onoceroids, a previously unreported sesquiterpene hydroquinone, of which the biosynthesis involves a Pyr4-family terpene cyclase, was obtained. Our genome mining tool has broad applicability in fungal genome mining and can serve as a beneficial platform for accessing diverse, unexploited natural products.

Data mining and safety analysis of avatrombopag: a retrospective pharmacovigilance study based on the US food and drug administration's adverse event reporting system.

With its increasing use in the treatment of thrombocytopenia, avatrombopag's associated adverse events (AEs) pose a major challenge to its clinical application. This study aims to comprehensively study AEs associated with avatrombopag by using real-world evidence. We curated AE reports for avatrombopag from the first quarter of 2018 to the fourth quarter of 2023 in the US Food and Drug Administration's Adverse Event Reporting System (FAERS) database. AEs were coded using the Medical Dictionary for Regulatory Activities of Preferred Terms and System Organ Classes. The reporting odds ratio, proportional reporting ratio, Bayesian confidence propagation neural network, and multi-item Gamma-Poisson Shrinker were used to investigate the relationship between avatrombopag and AE reports. Among 9,060,312 reported cases in the FAERS database, 1211 reports listed avatrombopag as "primary suspected" drug. Disproportionality analysis identified 44 preferred terms across 17 organ systems met the criteria for at least one of the four algorithms. The most commonly reported AEs were platelet count decreased (20.2%), headache (16.7%), platelet count increased (11.9%), platelet count abnormal (6.3%), contusion (2.7%), pulmonary embolism (2.3%), and deep vein thrombosis (2.1%). Unexpected AEs such as seasonal allergy, rhinorrhea, antiphospholipid syndrome, ear discomfort, and photopsia were also observed. Excluding the other serious outcomes, hospitalization (34.6%) was the most frequently reported serious outcome, followed by death (15.4%). Most reported AEs occurred within the first 2 days of initiating avatrombopag therapy, and the median onset time was 60 days. We identified new and unexpected AEs with clinical use of avatrombopag, and our results may provide valuable information for clinical monitoring and identifying risks associated with avatrombopag.

Adverse event signal mining and serious adverse event influencing factor analysis of fulvestrant based on FAERS database.

Fulvestrant, as the first selective estrogen receptor degrader, is widely used in the endocrine treatment of breast cancer. However, in the real world, there is a lack of relevant reports on adverse reaction data mining for fulvestrant. To perform data mining on adverse events (AEs) associated with fulvestrant and explore the risk factors contributing to severe AEs, providing a reference for the rational use of fulvestrant in clinical practice. Retrieved adverse event report information associated with fulvestrant from the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database, covering the period from market introduction to September 30, 2023. Suspicious AEs were screened using the reporting odds ratio (ROR) and proportional reporting ratio methods based on disproportionality analysis. Univariate and multivariate logistic regression analyses were conducted on severe AEs to explore the risk factors associated with fulvestrant-induced severe AEs. A total of 6947 reports related to AEs associated with fulvestrant were obtained, including 5924 reports of severe AEs and 1023 reports of non-severe AEs. Using the disproportionality analysis method, a total of 210 valid AEs were identified for fulvestrant, with 45 AEs (21.43%) not listed in the product labeling, involving 11 systems and organs. The AEs associated with fulvestrant were sorted by frequency of occurrence, with neutropenia (325 cases) having the highest number of reports. By signal strength, injection site pruritus showed the strongest signal (ROR = 658.43). The results of the logistic regression analysis showed that concurrent use of medications with extremely high protein binding (≥ 98%) is an independent risk factor for severe AEs associated with fulvestrant. Age served as a protective factor for fulvestrant-related AEs. The co-administration of fulvestrant with CYP3A4 enzyme inhibitors did not show statistically significant correlation with the occurrence of severe AEs. Co-administration of drugs with extremely high protein binding (≥ 98%) may increase the risk of severe adverse reactions of fulvestrant. Meanwhile, age (60-74 years) may reduce the risk of severe AEs of fulvestrant. However, further clinical research is still needed to explore and verify whether there is interaction between fulvestrant and drugs with high protein binding through more clinical studies.

An innovative method to strengthen evidence for potential drug safety signals using Electronic Health Records.

Reports from spontaneous reporting systems (SRS) are hypothesis generating. Additional evidence such as more reports is required to determine whether the generated drug-event associations are in fact safety signals. However, underreporting of adverse drug reactions (ADRs) delays signal detection. Through the use of natural language processing, different sources of real-world data can be used to proactively collect additional evidence for potential safety signals. This study aims to explore the feasibility of using Electronic Health Records (EHRs) to identify additional cases based on initial indications from spontaneous ADR reports, with the goal of strengthening the evidence base for potential safety signals. For two confirmed and two potential signals generated by the SRS of the Netherlands Pharmacovigilance Centre Lareb, targeted searches in the EHR of the Leiden University Medical Centre were performed using a text-mining based tool, CTcue. The search for additional cases was done by constructing and running queries in the structured and free-text fields of the EHRs. We identified at least five additional cases for the confirmed signals and one additional case for each potential safety signal. The majority of the identified cases for the confirmed signals were documented in the EHRs before signal detection by the Dutch Medicines Evaluation Board. The identified cases for the potential signals were reported to Lareb as further evidence for signal detection. Our findings highlight the feasibility of performing targeted searches in the EHR based on an underlying hypothesis to provide further evidence for signal generation.

Key issues in Japan's public health centers to prepare for future pandemics: a text mining study using a topic model.

BACKGROUND: In Japan, over 450 public health centers played a central role in the operation of the local public health system in response to the COVID-19 pandemic. This study aimed to identify key issues for improving the system for public health centers for future pandemics. METHODS: We conducted a cross-sectional study using an online questionnaire. The respondents were first line workers in public health centers or local governments during the pandemic. We solicited open-ended responses concerning improvements needed for future pandemics. Issues were identified from these descriptions using morphological analysis and a topic model with KHcoder3.0. The number of topics was estimated using Perplexity as a measure, and Latent Dirichlet Allocation for meaning identification. RESULTS: We received open-ended responses from 784 (48.6%) of the 1,612 survey respondents, which included 111 physicians, 330 nurses, and 172 administrative staff. Morphological analysis processed these descriptions into 36,632 words. The topic model summarized them into eight issues: 1) establishment of a crisis management system, 2) division of functions among public health centers, prefectures, and medical institutions, 3) clear role distribution in public health center staff, 4) training of specialists, 5) information sharing system (information about infectious diseases and government policies), 6) response to excessive workload (support from other local governments, cooperation within public health centers, and outsourcing), 7) streamlining operations, and 8) balance with regular duties. CONCLUSIONS: This study identified key issues that need to be addressed to prepare Japan's public health centers for future pandemics. These findings are vital for discussions aimed at strengthening the public health system based on experiences from the COVID-19 pandemic.

A method for mining condition-specific co-expressed genes in Camellia sinensis based on k-means clustering.

BACKGROUND: As one of the world's most important beverage crops, tea plants (Camellia sinensis) are renowned for their unique flavors and numerous beneficial secondary metabolites, attracting researchers to investigate the formation of tea quality. With the increasing availability of transcriptome data on tea plants in public databases, conducting large-scale co-expression analyses has become feasible to meet the demand for functional characterization of tea plant genes. However, as the multidimensional noise increases, larger-scale co-expression analyses are not always effective. Analyzing a subset of samples generated by effectively downsampling and reorganizing the global sample set often leads to more accurate results in co-expression analysis. Meanwhile, global-based co-expression analyses are more likely to overlook condition-specific gene interactions, which may be more important and worthy of exploration and research. RESULTS: Here, we employed the k-means clustering method to organize and classify the global samples of tea plants, resulting in clustered samples. Metadata annotations were then performed on these clustered samples to determine the "conditions" represented by each cluster. Subsequently, we conducted gene co-expression network analysis (WGCNA) separately on the global samples and the clustered samples, resulting in global modules and cluster-specific modules. Comparative analyses of global modules and cluster-specific modules have demonstrated that cluster-specific modules exhibit higher accuracy in co-expression analysis. To measure the degree of condition specificity of genes within condition-specific clusters, we introduced the correlation difference value (CDV). By incorporating the CDV into co-expression analyses, we can assess the condition specificity of genes. This approach proved instrumental in identifying a series of high CDV transcription factor encoding genes upregulated during sustained cold treatment in Camellia sinensis leaves and buds, and pinpointing a pair of genes that participate in the antioxidant defense system of tea plants under sustained cold stress. CONCLUSIONS: To summarize, downsampling and reorganizing the sample set improved the accuracy of co-expression analysis. Cluster-specific modules were more accurate in capturing condition-specific gene interactions. The introduction of CDV allowed for the assessment of condition specificity in gene co-expression analyses. Using this approach, we identified a series of high CDV transcription factor encoding genes related to sustained cold stress in Camellia sinensis. This study highlights the importance of considering condition specificity in co-expression analysis and provides insights into the regulation of the cold stress in Camellia sinensis.