Mapping the biomedical sciences using Medical Subject Headings: a comparison between MeSH co-assignments and MeSH citation pairs

MeSH2Matrix: combining MeSH keywords and machine learning for biomedical relation classification based on PubMed

Biomedical relation classification has been significantly improved by the application of advanced machine learning techniques on the raw texts of scholarly publications. Despite this improvement, the reliance on large chunks of raw text makes these algorithms suffer in terms of generalization, precision, and reliability. The use of the distinctive characteristics of bibliographic metadata can prove effective in achieving better performance for this challenging task. In this research paper, we introduce an approach for biomedical relation classification using the qualifiers of co-occurring Medical Subject Headings (MeSH). First of all, we introduce MeSH2Matrix, our dataset consisting of 46,469 biomedical relations curated from PubMed publications using our approach. Our dataset includes a matrix that maps associations between the qualifiers of subject MeSH keywords and those of object MeSH keywords. It also specifies the corresponding Wikidata relation type and the superclass of semantic relations for each relation. Using MeSH2Matrix, we build and train three machine learning models (Support Vector Machine [SVM], a dense model [D-Model], and a convolutional neural network [C-Net]) to evaluate the efficiency of our approach for biomedical relation classification. Our best model achieves an accuracy of 70.78% for 195 classes and 83.09% for five superclasses. Finally, we provide confusion matrix and extensive feature analyses to better examine the relationship between the MeSH qualifiers and the biomedical relations being classified. Our results will hopefully shed light on developing better algorithms for biomedical ontology classification based on the MeSH keywords of PubMed publications. For reproducibility purposes, MeSH2Matrix, as well as all our source codes, are made publicly accessible at https://github.com/SisonkeBiotik-Africa/MeSH2Matrix .

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.

Gender differences in citation sentiment: A case study in life sciences and biomedicine

In this study, we investigated whether female and male authors in the field of life sciences and biomedicine differed in their tendency for citation and citation sentiment. The data comprised two sets, cited set and citing set. Cited set comprised 17,237 articles whereas citing set comprised 115,935 articles. The cited set which is from the area Life Sciences & Biomedicine and published during 2012–2016 was retrieved from the Web of Science Medline. The citing set and its citation contexts were retrieved using the Colil database. The analysis was done using a combination of homophily analysis, regression analysis and a chi-square test. The covariates in the regression analyses were features related to authors, journal, institution, country and abstract readability. The homophily analysis showed a significant tendency for female (8%) and male (14%) authorship teams to cite papers by the same gender composition teams. In addition, the results of regression analysis (Model 1) and pairwise comparisons showed that male-authored papers received a significant higher positive sentiment compared with female-authored papers. The results of regression analysis (Model 2) showed a small significant positive association between gender similarity of cited and citing authorship teams and the sentiment score. However, further analysis using the chi-square test showed a significant lower tendency for women to use positive terms when citing the research findings of papers with the same gender composition. Men, in contrast, used significantly more positive terms when citing papers with the same gender composition. Finally, lay summary for a cited paper, country similarity and the venue of cited publication when it was a mega journal had a positive significant association with the sentiment score received.