Conduct Data Mining Research Articles

Drosophila glial system: an approach towards understanding molecular complexity of neurodegenerative diseases.

Glia is pivotal in regulating neuronal stem cell proliferation, functioning, and nervous system homeostasis, significantly influencing neuronal health and disorders. Dysfunction in glial activity is a key factor in the development and progression of brain pathology. However, a deeper understanding of the intricate nature of glial cells and their diverse role in neurological disorders is still required. To this end, we conducted data mining to retrieve literature from PubMed and Google Scholar using the keywords: glia, Drosophila, neurodegeneration, and mammals. The retrieved literature was manually screened and used to comprehensively understand and present the different glial types in Drosophila, i.e., perineurial, subperineurial, cortex, astrocyte-like and ensheathing glia, their relevance with mammalian counterparts, mainly microglia and astrocytes, and their potential to reveal complex neuron-glial molecular networks in managing neurodegenerative processes.

An "R-spondin code" for multimodal signaling ON-OFF states.

R-spondins (RSPOs) are a family of secreted proteins and stem cell growth factors that are potent co-activators of Wnt signaling. Recently, RSPO2 and RSPO3 were shown to be multifunctional, not only amplifying Wnt- but also binding BMP- and FGF receptors to downregulate signaling. The common mechanism underlying these diverse functions is that RSPO2 and RSPO3 act as "endocytosers" that link transmembrane proteins to ZNRF3/RNF43 E3 ligases and trigger target internalization. Thus, RSPOs are natural protein targeting chimeras for cell surface proteins. Conducting data mining and cell surface binding assays we report additional candidate RSPO targets, including SMO, PTC1,2, LGI1, ROBO4, and PTPR(F/S). We propose that there is an "R-spondin code" that imparts combinatorial signaling ON-OFF states of multiple growth factors. This code involves the modular RSPO domains, notably distinct motifs in the divergent RSPO-TSP1 domains to mediate target interaction and internalization. The RSPO code offers a novel framework for the understanding how diverse signaling pathways may be coordinately regulated in development and disease.

Adverse event signal mining and severe adverse event influencing factor analysis of Lumateperone based on FAERS database.

Lumateperone has been approved by the Food and Drug Administration (FDA) for the treatment of schizophrenia in adults since 2019, however, there is still a lack of data report on adverse reactions in real-world settings. Conducting data mining on adverse events (AEs) associated with Lumateperone and investigating the risk factors for serious AEs can provide valuable insights for its clinical practice. AE reports in the FDA Adverse Event Reporting System (FAERS) from 2019 Q4 (FDA approval of Lumateperone) to 2024 Q1 were collected and analyzed. Disproportionality in Lumateperone-associated AEs was evaluated using the following parameters: Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-item Gamma Poisson Shrinker (MGPS). Univariate and multivariate logistic regression analyses were conducted to identify the risk factors for Lumateperone-induced severe AEs. A total of 2,644 reports defined Lumateperone as the primary suspected drug was collected, including 739 reports classified as severe AEs and 1905 reports as non-severe AEs. The analysis revealed that 130 preferred terms (PTs) with significant disproportionality were based on the four algorithms, 67 (51.53%) of which were not included in the product labeling, affecting 6 systems and organs. In addition, dizziness (81 cases) was the most reported Lumateperone-associated severe AEs, and tardive dyskinesia showed the strongest signal (ROR = 186.24). Logistic regression analysis indicated that gender, bipolar II disorder, and concomitant drug use are independent risk factors for Lumateperone-associated severe AEs. Specifically, female patients had a 1.811-fold increased risk compared with male patients (OR = 1.811 [1.302, 2.519], p = 0.000), while patients with bipolar II disorder had a 1.695-fold increased risk compared with patients diagnosed with bipolar disorder (OR = 1.695 [1.320, 2.178], p = 0.000). Conversely, concomitant use of CYP3A4 inhibitors or drugs metabolized by CYP3A4 was associated with a decreased risk of severe AEs (OR = 0.524 [0.434, 0.633], P = 0.000). Collectively, this study provides critical insights into the safety profile of Lumateperone. It highlights the need for cautious use in high-risk populations, such as females and individuals with bipolar II disorder, and emphasizes the importance of monitoring for AEs, including dizziness and tardive dyskinesia. Healthcare also should remain alert to potential AEs not listed in the prescribing information to ensure medical safety.

Diffusion of fashion trend information: a study on fashion image mining from various sources

The advancement in the internet and mobile technologies has substantially altered information diffusion in modern society, creating a diverse environment for generating and sharing various forms of information. Specifically, the emergence of new information sources, such as influencers and online communities, has significantly influenced the formation of consumer opinion. We highlight the changes that have occurred in the diffusion of fashion trend information. To do this, we conducted data mining, which involved three main steps: data preprocessing, specifically converting image data (including images from the 2022 F/W season runway collection, fashion influencer outfits, and best items from online fashion retailers) into textual data; data mining analysis (quantitative analysis); and data post-processing. As a result, we found that even items with low or no appearance on the runway held significance in the best item data or fashion influencer outfits. Specifically, the best items on online fashion retailers, reflecting popular fashion trends, had greater similarity to fashion influencer outfits. However, similarities in silhouette attributes were found among runway collections, fashion influencer outfits, and best items data. This study holds great significance because it focuses on fashion items genuinely consumed by the mainstream consumers rather than only focusing on the four major runway collections. Furthermore, these findings offer valuable insights for merchandising and trend forecasting, emphasizing the importance of selectively utilizing fashion trend information in the planning of fashion products.

Construction of a network intelligence platform for privacy protection and integrated big data mining

MOTIVATION: With the enhancement of people’s awareness of the protection of personal privacy information, how to provide better services on the premise of protecting users’ privacy has become an urgent problem to be solved. Therefore, it is a necessary motivation to build a network intelligent platform for privacy protection and integrated big data mining. OBJECTIVE: In view of the existing network platform of data privacy leakage, low efficiency of data mining and user satisfaction is not high, this paper will adopt advanced privacy technology, to ensure the confidentiality of users’ personal information and security, to enhance the user trust and use experience, to better meet the needs of users. METHODS: In order to better protect the privacy of users, the network intelligent platform should adopt more advanced privacy protection technology. This paper uses the differential privacy algorithm to reduce the risk of data leakage and abuse, and ensure the accuracy and efficiency of data analysis and mining. In the design of the platform, the performance of the platform is fully taken into account to realize the secure storage and efficient processing of data, with good scalability and flexibility to meet the growing user needs and business needs. The performance of the network intelligent platform is also analyzed by experimental simulation. RESULT: The experimental results of this article indicated that in a network intelligent platform based on privacy protection and integrated big data mining, its data transmission encryption score was 9.5; the data storage encryption score was 9.8; the score of access control mechanism was 9.3; the privacy protection score was 9.6; the response time was 80 ms; the processing speed was 121GB/h; the user satisfaction rating was 6.6. CONCLUSION: This indicated that the network intelligent platform had good platform performance and user friendliness while ensuring data security and privacy protection. It could efficiently conduct data mining and ensure data security and privacy.

Bibliometric Analysis of Geographic Information System Impact on Urban Areas Management

Geographic Information System is a global phenomenon that has revolutionized technology and is receiving much attention currently. To objectively conceptualize the research context of Geographic Information System (GIS) impact on human settlement, bibliometrix and biblioshiny software packages were applied to conduct data mining and qualitative analysis. Research papers including journals and articles in the fields of GIS and Urban Areas management spanning from 2000 to 2023 were extracted. This scientific data was extracted on 8TH January 2023 in the Publish or Perish core collection database. The results indicated that: publications fell from 2000 to 2001, however, they increased a bit in 2002. There was no significant change until 2007 when there was a slight increase in publications up to 2008. Geographic Information Systems, flood-prone areas, urban areas, suitable areas, rural areas and maps are the most frequently used keywords in the field of GIS and urban areas management in recent years. Consequently, the paper concludes that the research directions on GIS impact on human settlement management in future include the following processes and mechanisms: the application of state-of-the-art GIS technologies, the introduction of GIS crime monitoring innovations for urban centers, robust GIS simulation innovations to enhance urban resilience and digitization of human settlements for promotion of sustainable, Smart and Blue Cities. Finally, research into the creation of geo-spatial database platforms and data infrastructure for human or urban centers should be promoted. This paper aims to review the literature on the significance of GIS as a powerful tool in addressing the complexities and challenges inherent in urban environments.

Analytical Comparison of Lung Cancer Classification Using K-Nearest Neighbor and Naïve Bayes Algorithms

Lung cancer stands as a significant global contributor to human mortality, constituting 25% of all cancer-related deaths in 2021. Its elusive nature, often devoid of early symptoms in a quarter of diagnosed cases, poses a challenge for timely detection. Unlike some other cancers, lung cancer remains hidden from the naked eye, with its symptoms often masquerading as those of other ailments like bronchitis, asthma, or persistent coughs. Early diagnosis is pivotal for effective treatment and increased survival rates. In light of the pressing nature of the situation, this study investigates the prediction of lung cancer by using data mining tools. It is essential to conduct data mining, which is a process that involves searching for patterns and trends inside vast data repositories to discover valuable insights. Within this context, classification emerges as a fundamental aspect that discerns objects based on their distinctive characteristics. A comparative study is undertaken to address the complexities associated with lung cancer classification, focusing on the K-Nearest Neighbor (KNN) and Naïve Bayes Classifier (NBC) algorithms. Through the utilization of a dataset that contains one thousand instances and twenty-four criteria, the purpose of this study is to determine which algorithm is preferable in the categorization of lung cancer. Upon analysis, the study yields noteworthy results. The KNN algorithm exhibits an accuracy rate of 98.34%, surpassing the NBC algorithm's accuracy of 89.37%. Consequently, this research concludes that, in lung cancer classification, the KNN algorithm outperforms the Naïve Bayes algorithm. These findings promise to enhance the efficacy of early lung cancer detection, potentially saving numerous lives through improved classification methods.

The use of deep learning integrating image recognition in language analysis technology in secondary school education.

This work aims to investigate the application of advanced deep learning algorithms and image recognition technologies to enhance language analysis tools in secondary education, with the goal of providing educators with more effective resources and support. Based on artificial intelligence, this work integrates data mining techniques related to deep learning to analyze and study language behavior in secondary school education. Initially, a framework for analyzing language behavior in secondary school education is constructed. This involves evaluating the current state of language behavior, establishing a framework based on evaluation comments, and defining indicators for analyzing language behavior in online secondary school education. Subsequently, data mining technology and image and character recognition technology are employed to conduct data mining for online courses in secondary schools, encompassing the processing of teaching video images and character recognition. Finally, an experiment is designed to validate the proposed framework for analyzing language behavior in secondary school education. The results indicate specific differences among the grouped evaluation scores for each analysis indicator. The significance p values for the online classroom discourse's speaking rate, speech intelligibility, average sentence length, and content similarity are -0.56, -0.71, -0.71, and -0.74, respectively. The aim is to identify the most effective teaching behaviors for learners and enhance the support for online course instruction.

Comparative Analysis of Facebook Page Marketing: Case Study of Chinese and Norwegian Agricultural Companies in Myanmar

Facebook is powerful business tool that businesses to establish their online presence on one of the world’s largest social media platforms exploring foreign markets, accelerating internationalization, and improving performance in foreign markets. Facebook not only offers a range of advertising programs, but it also lets business customize their own profile and public relevant content to build their own community around their brand. The purpose of this study is to explore an effective Facebook marketing strategy applied by Chinese and Norwegian companies, identifying differences and similarities in their approaches. The study conduct data mining to track and summarize engagement activities on the official Facebook pages for a period of one-year (starting from January 1 to December 31, 2023) and analyzed total 191 posts of two foreign-based companies in Myanmar, 177 posts of a Norwegian company and 14 posts of a Chinese company. This total 191 posts, the authors manually entered into Microsoft excel sheet and then converted into a format for analysis. The analysis is conducted using Microsoft Excel to compare Facebook engagement and to apply Arman’s engagement rate formula for evaluating the quality of Facebook engagement. The results show that Chinese and Norwegian companies effectively used the product information text with images on Facebook is popular with customers, generating increasing likes, shares and comments and improving page engagement, which boosts customer purchases. Engagement Rate is vital for Facebook optimization, indicating a page’s influence on its followers, with the study revealing that Chinese Facebook page have higher engagement rate than Norwegian Facebook page. Additionally, Norwegian company effectively maintain customer relationships through live chat automated responses, providing timely information, reduction uncertainty, and increasing sales by understanding customer needs and potential purchasing behaviors.

Research on the Reform and Development of Ideological and Political Theory Classes for College Students in the Context of Practical Parenting

Abstract In the context of practical education, deepening the reform of quality education and implementing the fundamental task of cultivating moral integrity have become the guidelines and the basis for the development of curriculum reform in colleges and universities. This study conducts data mining based on the association rule model on the influencing factors of the reform of the Civic and Political Theory Course in colleges and universities to obtain the key influencing factors in the process of the reform of the theory course. Then, under the perspective of deep learning, the idea of practical parenting is integrated, and the structural framework of Civic and Political Theory course reform is designed from four directions: learning theme, learning objectives, learning activities, and evaluation feedback, and finally, the course reform method of this paper is examined through teaching practice. The results show that the influence weights of self-factors, school education factors, family factors, and social factors are 0.3022, 0.3521, 0.1381, and 0.2076, respectively, with self-factors and school education as the key influencing factors. After the teaching practice, the P-value of each dimension of the students under the curriculum reform based on this paper is less than 0.05, and the grade of the Civics and Political Science Theory course is improved from 81.47 to 90.45, which is an increase of 7.49 points compared with the class of the regular course. The reform method based on deep learning in this study can promote students’ development and improve their academic performance, which provides a reference for the research on the reform and development of the Civics and Political Science Theory course.

User Sentiment Analysis Based on Online Reviews -Taking the Forbidden City Afternoon Tea Restaurant as an Example

Nowadays, Internet consumption has become a familiar name, and there are more and more feedback channels on the production experience of consumers, and the content of evaluation also determines consumers' views on the goods to a certain extent. Therefore, the rational use of online comment contents can not only help consumers understand the basic information of products, but also help enterprises to better obtain the needs of consumers and provide more references for them. This paper selects the comment data of the Palace restaurant in Dianping software, and uses Python software to conduct data mining, emotion analysis, word frequency statistics and word cloud drawing. The conclusion shows that consumers have a positive emotional tendency towards the afternoon tea in the Palace Museum, with few negative comments. At the same time, consumers pay more attention to the dining location and food ingredients. Therefore, businesses can increase the improvement of dishes and environmental transformation through online reviews to better meet the needs of consumers. This research not only provides the basis for the evaluation of the business level, but also helps to enrich the consumer's understanding of the business.

An overview of big data mining and data privacy protection technologies

With the advent of the era of big data, data mining techniques have significantly improved their ability to extract valuable information from data. However, privacy dangers are growing. Consequently, securing the protection of personal privacy during the mining of massive amounts of data has become a significant challenge. This paper examines the relationship between data mining techniques and privacy protection measures through a review of the pertinent literature. It provides a concise analysis of the benefits and drawbacks of commonly utilized classification algorithms in data mining. In addition, it examines the interplay between data mining techniques and privacy protection and summarizes important privacy protection techniques. In addition, this paper provides a summary of the most important privacy protection methods. These techniques include data anonymization, association rule concealing, data perturbation, etc. By comprehending these privacy protection techniques, appropriate privacy safeguards can be selected to ensure the privacy and security of the data when conducting data mining.

Research on the innovation of the path of ideological and political education in universities based on data mining

Abstract This paper first describes the parallel association rule mining method for the big data implementation process. To perform similar item merging, each data item in the dataset is scanned, the dataset is divided, and then the similarity degree is calculated. And the dynamic support threshold is obtained based on the idea of genetic iteration of the genetic algorithm to obtain the optimal solution. In order to conduct data mining, the mining process is then transferred to the MapReduce computing platform while the data output from the Map step is parallel LZO compressed. Finally, in the age of big data, the ideological and political work in colleges is mined and analyzed using a data mining algorithm, and route innovation remedies are proposed. According to the current state of ideological and political education, 77.6% of respondents believe that the ideological and political management system is not ideal, and 61.48 percent believe that the ideological and political content is fragmented and that data-based information makes it challenging to supervise. The immaturity of new media development and the growth of technological challenges amounted to 46%. After creative approaches, ideological and political education teaching satisfaction has grown by 15-20 percentage points. Ideological and political education at universities under the impact of big data should grab the possibilities and overcome the difficulties to achieve sustainability and innovative development.

A study on the emotional and attitudinal behaviors of social media users under the sudden reopening policy of the Chinese government.

Since the outbreak of the COVID-19 pandemic, the Chinese government has implemented a series of strict prevention and control policies to prevent the spread of the virus. Recently, the Chinese government suddenly changed its approach and lifted all prevention and control measures. This sudden change in policy is expected to lead to a widespread outbreak of COVID-19 in China, and the public and local governments are not adequately prepared for the unknown impact on society. The change in the "emergency" prevention and control policy provides a unique research perspective for this study. The purpose of this study is to analyze the public's attitudes and emotional responses to COVID-19 under the sudden opening policy, identify the key factors that contribute to these attitudes and emotions, and propose solutions. In response to this sudden situation, we conducted data mining on topics and discussions related to the opening of the epidemic on Sina Weibo, collecting 125,686 interactive comments. We used artificial intelligence technology to analyze the attitudes and emotions reflected in each data point, identify the key factors that contribute to these attitudes and emotions, explore the underlying reasons, and find corresponding solutions. The results of the study show that in the face of the sudden release of the epidemic, the public mostly exhibited negative emotions and behaviors, with many people experiencing anxiety and panic. However, the gradual resumption of daily life and work has also led some people to exhibit positive attitudes. The significance of this study is to help the government and institutions understand the impact of policy implementation on users, and to enable them to adjust policies in a timely manner to respond to potential social risks. The government, emergency departments, and the public can all prepare for similar situations based on the conclusions of this study.

A Bibliometric Analysis of the Impact of Ecological Restoration on Carbon Sequestration in Ecosystems

Ecological restoration, as a vital means of effectively enhancing the carbon sink function of ecosystems, is currently the subject of active research by scientists. Researchers are actively exploring how to scientifically assess the response mechanisms of ecosystem carbon reservoirs during the process of ecological restoration. In this study, CiteSpace 6.1.(R3 and R6) literature visualization software was employed to conduct data mining on 1566 research articles published from 1996 to 2022, focusing on the impact of ecological restoration on ecosystem carbon reservoirs, as recorded in the Web of Science core database. The analysis involved visualizing various aspects, including the countries involved, research institutions, publication output, research hotspots, and cutting-edge research areas. The research indicates that China holds significant influence in the study of the impact of ecological restoration on ecosystem carbon reservoirs. The literature covers a wide range of research directions and encompasses rich content on the subject matter. The current research focuses on ecological restoration, and its impact on the carbon sink function of ecosystems mainly revolves around four key themes: “the carbon sequestration potential of ecological restoration”, “technological approaches to enhancing the carbon sink function of ecological restoration”, “the importance of assessing carbon sink in terrestrial ecosystems”, and “characteristics of carbon sources/sinks in terrestrial ecosystems”. Currently, the development of research findings on the impact of ecological restoration on the carbon reservoirs of ecosystems is progressing rapidly. Novel research theories, methodologies, and scientific techniques are emerging, necessitating the continuous monitoring and investigation of scholarship in this field. It is crucial to integrate ongoing global environmental-change factors, ensuring the continuity of research and observations and, thus, furnishing robust data support for the assessment and computation of ecosystem carbon sinks.

Big data mining on trace element geochemistry of sphalerite

The determination of ore genesis is a main challenge in ore deposit research. Advanced and rapid analytical techniques have given rise to the accumulation of massive amounts of geoscientific data. Therefore, it is imperative to conduct data mining on ore geochemical data to efficiently extract useful metallogenic information. In this contribution, 4095 sets of sphalerite trace element data from 86 deposits of different origins (VMS, MVT, porphyry, epithermal, SEDEX, and skarn) were compiled and analyzed. Factor analysis revealed the effects of physicochemical conditions on sphalerite trace element compositions. Specifically, the high Mn-Fe-Cu-Co-In but low As-Ga-Ge-Sb-Pb concentration of sphalerite is commonly related to decreasing pH or increasing temperature; high sulfur fugacity favors the entry of Fe and Co (but not Ga or Sn) into the sphalerite lattice; and high salinity causes enrichment in Cd-Ga-Ge-Mn, but depletion in Ag-Cu-In-Sb. Multivariate statistical analysis reveals that the Mn-Ge-Sn contents in sphalerite have good potential to differentiate among ore genetic types, the Mn-Ge-In are used to determine between magmatic-hydrothermal and non-magmatic-hydrothermal deposits. Furthermore, machine learning models demonstrate high accuracy of sphalerite trace element data in distinguishing ore types, i.e., 93.02 % (random forest) and 92.82 % (gradient boosting), and its reliability was validated by receiver operating characteristics. Additionally, the blind tests by machine learning on sphalerite trace elements indicate that the Qingshuitang deposit (in the Qin-Hang metallogenic belt, South China), have been an MVT deposit, which is also supported by its low-temperature and high-salinity fluids, wallrock alterations (esp. bariteization), and the obvious age distinction between mineralization and local magmatism. This study highlights that machine learning and multivariate statistical analysis on sphalerite trace-element data can differentiate metallogenic origin and conditions.

Chi nese Middle-class Travelers’ Characteristics and Their Perceptions of Japan's National Image

Prior to the COVID-19 pandemic, China was the largest source country in Japan’s inbound tourism market. The middle class, with its high consumption desire, was the main contributor of Chinese tourists to Japan, an important force driving the Japanese economy and influencing the spread of Japan’s national image in China through the Internet. For this study, I collected 4,785 text data from Japan travelogues on two major Chinese travel websites between January 2016 and March 2022, conducted data mining and content analysis, and combined the texts to reveal the basic characteristics of Chinese middle-class tourists to Japan, as well as their perceptions and evaluations of Japan’s national image. The results show a change in the travel needs of the Chinese middle class from shopping, etc., to diverse, specific, and individual travel experiences and an obvious willingness to revisit. Regarding their perceptions and evaluations of Japan's macro national image, Japan’s national capacity is mainly reflected in its culture and economic technology, while its national characteristics are mainly reflected in hospitability. Although most impressed with Japan’s environmental management, mainly its infrastructure and natural resources, the macro sentiment of the tourists is influenced by historical Sino-Japanese relations and reveals reservations about Japan. Japan’s micro national image, as perceived by the tourists through tourist attractions, mainly relates to activities, while their perceptions of the tourist environment are the richest, focusing on popular attractions and landscapes. Their perceptions of tourism facilities cover various aspects such as catering, accommodation, transportation, entertainment, and services. Furthermore, their micro-emotions are mainly reflected in their satisfaction and praise of the destination environment. These results have several implications for tourism marketing and national image promotion in the post-pandemic era, when outbound tourism has fully recovered.

Visual Analysis of Knowledge Networks for International Brands and Service Quality Based on Bibliometrix

In recent years, the research field of brand and service quality has been highly concerned by international scholars. Based on international literature, this paper analyzes the spatial distribution characteristics, dynamic and static structure and evolution characteristics of brand and service quality network. According to the relevant literature of Web of Science database as research samples, the Bibliometrix knowledge network visualization analysis software is used to conduct data mining on the research results and hot spots of brand and service quality, so as to provide theoretical reference for the theoretical research and practical exploration of this topic research field.

Marketing Decision Support System Based on Data Mining Technology

With the continuous development of business intelligence technology, the application research of decision support systems (DSSs) is deepening. In China, the work in this area started relatively late, and there are few DSS research cases to assist in marketing decision-making. Currently, marketing decision support systems have shortcomings in data integration, historical data, query functions, and data analysis. This article analyzes the characteristics of marketing decision-making, discusses the application of data warehouse, OLAP, and data mining technology in marketing decision support systems, and designs a marketing decision support system based on data mining technology. The system uses a BP neural network to conduct data mining marketing forecasting. A three-layer network model for marketing prediction is established, with sales time, product price, and customer purchasing power as network inputs and output as the sales volume of a certain type of product in different locations. The test results show that the average absolute percentage error of this method is 15.13%, and the prediction accuracy is high. Research shows that with the continuous development of data mining technology, the system cannot only help users conduct scientific and reasonable marketing decision-making analyses, making the marketing decision-making process more scientific and reasonable, but also can bring new ideas to enterprise decision-makers, promoting the continuous improvement and progress of the system.

A comparison of taxon-like schizotypal clusters of non-clinical individuals on polygenic scores for schizophrenia and related phenotypes

IntroductionSchizotypy is conceptualized to be on the continuum of the risk for psychosis. However, previous studies that used the dimensional approach to schizotypy failed to confirm the expected relations between schizotypal traits and polygenic risk scores (PRS) for schizophrenia. A taxonic approach is an alternative way of looking at schizotypy, but to the best of our knowledge it has not been used to explore the genetic architecture of this construct.ObjectivesThe present study aimed to fill this gap by comparing groups with different profiles of schizotypal traits on PRS for schizophrenia and related phenotypes.MethodsTo find clusters with different combinations of schizotypal traits, we conducted data mining of an ethnically homogeneous cohort of 1377 healthy individuals completed the Schizotypal Personality Questionnaire. Four clusters emerged, termed low, positive, negative, and high mixed schizotypy. Approximately equal groups from each cluster were selected for genotyping. After quality control, PRS for schizophrenia, depression, neuroticism, and educational attainment were calculated for 320 individuals (mean age = 31.74, SD 12.25 years, 59% women) based on the summary statistics of the largest genome-wide association studies of the respective traits. The groups from different clusters were then compared on PRS.ResultsThe schizotypy groups were similar in age and sex composition but differed in educational attainment and neuroticism (as measured by the Eysenck Personality Inventory), with the high mixed schizotypes having the lowest education and highest neuroticism scores among the schizotypy groups. There were no statistically significant differences between the groups in any PRS. However, the high mixed schizotypy group showed the largest PRS for schizophrenia, neuroticism, and depression. At a nominally significant level, it differed from negative and positive schizotypes in schizophrenia PRS and from low schizotypy in neuroticism PRS. The positive and negative schizotypal groups had non-significantly lower schizophrenia PRS than low schizotypy subjects.ConclusionsOur study showed no reliable difference between groups with different schizotypal profiles in PRS of schizophrenia and related phenotypes. At the same time, a number of trends were observed suggesting that only the high mixed schizotypy might be viewed as a condition with an elevated genetic risk of schizophrenia. This is in line with Meehl’s quasi-dimensional model of schizotypy and warrants further investigation in larger samples. This work was supported by the Russian Foundation for Basic Research under Grant 20-013-00230.Disclosure of InterestNone Declared