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Introduction Cancer is a major public health problem and a global leading cause of death where the screening, diagnosis, prediction, survival estimation, and treatment of cancer and control measures are still a major challenge. The rise of Artificial Intelligence (AI) and Machine Learning (ML) techniques and their applications in various fields have brought immense value in providing insights into advancement in support of cancer control. Methods A systematic and thematic analysis was performed on the Scopus database to identify the top 100 cited articles in cancer research. Data were analyzed using RStudio and VOSviewer.Var1.6.6. Results The top 100 articles in AI and ML in cancer received a 33 920 citation score with a range of 108 to 5758 times. Doi Kunio from the USA was the most cited author with total number of citations (TNC = 663). Out of 43 contributed countries, 30% of the top 100 cited articles originated from the USA, and 10% originated from China. Among the 57 peer-reviewed journals, the “Expert Systems with Application” published 8% of the total articles. The results were presented in highlight technological advancement through AI and ML via the widespread use of Artificial Neural Network (ANNs), Deep Learning or machine learning techniques, Mammography-based Model, Convolutional Neural Networks (SC-CNN), and text mining techniques in the prediction, diagnosis, and prevention of various types of cancers towards cancer control. Conclusions This bibliometric study provides detailed overview of the most cited empirical evidence in AI and ML adoption in cancer research that could efficiently help in designing future research. The innovations guarantee greater speed by using AI and ML in the detection and control of cancer to improve patient experience.

Background Mycetoma is a neglected tropical disease that attracts little attention in regard to research and publications and hence this study was undertaken to determine the trends and global scientific research output in mycetoma-related fields. Methods Mycetoma data were retrieved from the Web of Science (WoS) and Scopus databases. The MeSH Browser was used to extract relevant keywords. Biblioshiny software (R-studio cloud), VOSviewer v. 1.6.6 and SPSS software were used for data management. Results Research trends on mycetoma increased globally from 1999 to 2020. The results were 404 documents (4444 citations) in WoS and 513 documents (5709 citations) in Scopus, and the average number of citations per article was 11 in WoS and 11.13 in Scopus. There was a significant association between the total number of citations and the total citations per year in both WoS (r=0.833, p<0.0001) and Scopus (r=0.926, p<0.0001). Sudan, India, the Netherlands and Mexico were the top-ranking productive countries for mycetoma publications in WoS, while India, the USA and Mexico were the top-ranking countries in Scopus. Articles on mycetoma were mainly published in PLoS Neglected Tropical Diseases, the International Journal of Dermatology and the Journal of Clinical Microbiology. A. H. Fahal from the Mycetoma Research Centre, University of Khartoum, Sudan, had the highest number of citations in mycetoma research during 1999–2020, followed by W. W. J. van de Sande from the Erasmus Medical Centre, University of Rotterdam, the Netherlands, during 2003–2020. Conclusion The analysis provides insight into a global overview of Mycetoma research. In addition, the analysis holds a better understanding of the development trends that have emerged in Mycetoma over the past 21 years, which can also offer a scientific reference for future research.

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The splitting approach is developed for the numerical simulation of genetic regulatory networks with a stable steady-state structure. The numerical results of the simulation of a one-gene network, a two-gene network, and a p53-mdm2 network show that the new splitting methods constructed in this paper are remarkably more effective and more suitable for long-term computation with large steps than the traditional general-purpose Runge-Kutta methods. The new methods have no restriction on the choice of stepsize due to their infinitely large stability regions.