IMA: Identifying disease-related genes using MeSH terms and association rules

Brand Marketing Leveraging the Advantage of Emoji Pack Relying on Association Rule Algorithm in Data Mining Technology

The random and disordered configuration of expression package image, text, and context is in line with the emotional consumption trend of young groups, so it has become a kind of image symbol favored by young groups. How to integrate the text and images related to emoticons and carry out emotion analysis in a specific consumer environment is the focus of attention. In view of these needs and defects, this study uses the association rule algorithm in data mining technology to explore brand marketing targeting different consumers. Taking the nonverbal symbols on social media as the research object, this study collects and mines the download ranking and appreciation of the expression package of “WeChat expression open platform.” The persistence of users' use of expression package has the characteristics of power distribution. When choosing, there are sprouting attributes and dynamic and serialized selection preferences. The advantages of emoticon package are used, brand communication and marketing are strengthened, and effective brand marketing analysis is implemented. The simulation results show that by taking advantage of the strong communication and appeal of the expression package, the brand will help to expand the contact surface of the marketing brand concept. Secondly, as a gathering community, expression packs are divided into different circle groups between and within groups, which helps brand marketing realize precision marketing based on “strong connection” with the help of expression packs and promote the occurrence of consumer behavior.

Connections and Biases in Health Equity and Culture Research: A Semantic Network Analysis

Health equity is a rather complex issue. Social context and economical disparities, are known to be determining factors. Cultural and educational constrains however, are also important contributors to the establishment and development of health inequities. As an important starting point for a comprehensive discussion, a detailed analysis of the literature corpus is thus desirable: we need to recognize what has been done, under what circumstances, even what possible sources of bias exist in our current discussion on this relevant issue. By finding these trends and biases we will be better equipped to modulate them and find avenues that may lead us to a more integrated view of health inequity, potentially enhancing our capabilities to intervene to ameliorate it. In this study, we characterized at a large scale, the social and cultural determinants most frequently reported in current global research of health inequity and the interrelationships among them in different populations under diverse contexts. We used a data/literature mining approach to the current literature followed by a semantic network analysis of the interrelationships discovered. The analyzed structured corpus consisted in circa 950 articles categorized by means of the Medical Subheadings (MeSH) content-descriptor from 2014 to 2021. Further analyses involved systematic searches in the LILACS and DOAJ databases, as additional sources. The use of data analytics techniques allowed us to find a number of non-trivial connections, pointed out to existing biases and under-represented issues and let us discuss what are the most relevant concepts that are (and are not) being discussed in the context of Health Equity and Culture.

Finding Gene Associations by Text Mining and Annotating it with Gene Ontology

Digitalization of the research articles and their maintenance in a database was the first stage toward the development of biomedical research. With the large amounts of research being published daily, it has created a large gap in accessing all the articles for review to a given problem. To understand any biological process, an insight into the role of each element in the genome is essential. But with this gap in manual curation of literature, there are chances that important biological information may be lost. Hence, text mining plays an important role in bridging this gap and extracting important biological information from the text, finding associations among them and predicting annotations. An annotation may be gene, gene products, gene names, their physical and functional characteristics, and so on. The process of annotations may be classified as structural annotation, functional annotation, and relational annotation. In this chapter, a basic protocol utilizing text mining to extract biological information and predict their functional role based on Gene Ontology is provided.

Clustering datasets with demographics and diagnosis codes

Clustering data derived from Electronic Health Record (EHR) systems is important to discover relationships between the clinical profiles of patients and as a preprocessing step for analysis tasks, such as classification. However, the heterogeneity of these data makes the application of existing clustering methods difficult and calls for new clustering approaches. In this paper, we propose the first approach for clustering a dataset in which each record contains a patient's values in demographic attributes and their set of diagnosis codes. Our approach represents the dataset in a binary form in which the features are selected demographic values, as well as combinations (patterns) of frequent and correlated diagnosis codes. This representation enables measuring similarity between records using cosine similarity, an effective measure for binary-represented data, and finding compact, well-separated clusters through hierarchical clustering. Our experiments using two publicly available EHR datasets, comprised of over 26,000 and 52,000 records, demonstrate that our approach is able to construct clusters with correlated demographics and diagnosis codes, and that it is efficient and scalable.