Predicting Scientific Research Impacts in Biotechnology by Machine Learning Algorithms

purpose: Research impact is a key concern for stakeholders, as it reflects the beneficial and profitable applications of research across multiple dimensions, including society, economy, environment, culture, and health. This study aims to analyze the interrelationships among variables influencing scientific outputs and to identify the most effective machine learning algorithms for predicting their scientific, social, and economic impacts.Methodology: The current research is applied in purpose and descriptive in method, utilizing a scientometric approach. This study aims to explore the relationship between the quantity of scientific publications and scientific cooperation, as well as the scientific, social, and economic impact of Iran's scientific contributions in the field of biotechnology. Additionally, it seeks to determine which machine learning algorithms are more effective in predicting the impact of scientific outputs across various dimensions. The research focuses on Iran's biotechnology scientific outputs indexed in the Scopus database from 2003 to 2024. The data, extracted on 21 January 2024, were analyzed using the SciVal analytical database. In this research, Pearson's correlation coefficient and the R software package were used to examine the relationships between the studied indicators. Machine learning algorithms, including multiple linear regression, nearest neighbors, decision trees, random forests, and gradient boosting, were applied and evaluated as predictive models. The Python programming language was employed to conduct tests and implement these algorithms.Findings: The findings of this study showed that Iran's scientific outputs in this field in the period from 2003 to 2023 had increased 36 times, which is considered extremely high progress. A positive and significant relationship was observed between international collaboration and various impact indicators, including citation counts, Field-Weighted Citation Impact, Output in Top 10% Citation Percentiles, Patent-Citations Count, Patent-Citations per Scholarly Output, Scholarly Output cited by Patents, Patents Count, Views Count, Output in Top 10% Views Percentiles, Views per Publication, and Field-Weighted Views Impact. The index of academic collaboration also shows a positive and significant relationship with several indicators, including citation counts, Field-Weighted Citation Impact, Output in the Top 10% Citation Percentiles, Publications in the Top 10% Journal Percentiles (by Cite Score Percentile), Patents Count, Scholarly Output cited by Patents, Views Count, and Field-Weighted Views Impact. Academic-government collaboration is also positively and significantly correlated with three indicators citations per publication, Patent-Citations Count, and Patent-Citations per Scholarly Output. In the case of the impact of the studied scientific outputs, citation counts are positively and significantly associated with several indicators, including Scholarly Output cited by Patents, Patents Count, Views Count, Views per Publication, and the Field-Weighted Views Impact of the field of biotechnology. In terms of economic impact, the result indicated that the number of patent citations is a key representative indicator. This indicator showed positive and significant relationships with other metrics, including academic Collaboration, international Collaboration, citation counts, citations per Publications, Field-Weighted Citation Impact, Views Count positive and significant relationship in Output in Top 10% Views Percentiles, Views per Publication and Field-Weighted Views Impact in the field of biotechnology. Regarding social impact, the analysis revealed that Views Count is positively and significantly correlated with several indicators, including citation counts, Field-Weighted Citation Impact, the number of patent citations, Patent-Citations per Scholarly Output, Scholarly Output cited by Patents and Patents Count in biotechnology. Based on the results, multivariate linear regression demonstrated higher accuracy and a lower standard deviation, making it a more effective model for predicting the scientific, technological, and social impact of Iran's scientific outputs in biotechnology.Conclusion: International cooperation is the most significant factor influencing the quality of articles, including metrics such as citations, views, and applicability. Therefore, it is crucial to explore and implement strategies to enhance such collaborations. To better assess the effectiveness of research, it is recommended to employ a diverse set of indicators during both quantitative and qualitative evaluations of articles. For policymakers in science and technology, the primary focus should be on value creation and generating added value, particularly in the economic sector. This is essential despite the observed quantitative and qualitative growth in Iran's scientific outputs.