Exploratory Analysis of Topic Interests and Their Evolution in Bioinformatics Research Using Semantic Text Mining and Probabilistic Topic Modeling

Bioinformatics, which has developed rapidly in recent years with the collaborative contributions of fields biology and informatics, provides a deeper perspective on analysis understanding complex biological data. In this regard, bioinformatics an interdisciplinary background rich literature terms domain-specific studies. Providing holistic picture research by analyzing major topics their trends developmental stages is critical for field. From perspective, study aimed to analyze last 50 studies (a total 71,490 articles) using automated text-mining methodology based probabilistic topic modeling reveal main topics, trends, evolution As result, 24 that reflect focuses field were identified. Based discovered temporal tendencies from 1970 until 2020, periods divided into seven phases, “newborn” “wisdom” stages. Moreover, findings indicated increase popularity “Statistical Estimation”, “Data Analysis Tools”, “Genomic Data”, “Gene Expression”, “Prediction”. The results revealed that, studies, interest innovative computing data methods artificial intelligence machine learning gradually increased, thereby marking significant improvement contemporary tools techniques prediction.