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Molecular biology experiments can confirm RNA\u2013RNA interactions to facilitate the exploration of their biological functions, but they are expensive and time-consuming. Machine learning models can predict potential RNA\u2013RNA interactions, which provide candidates for molecular biology experiments to save a lot of time and cost. Using a set of suitable features to represent the sample is crucial for training powerful models, but there is a lack of effective feature representation for RNA\u2013RNA interaction. This study proposes a novel feature representation method with information enhancement and dimension reduction for RNA\u2013RNA interaction (named RNAI-FRID). Diverse base features are first extracted from RNA data to contain more sample information. Then, the extracted base features are used to construct the complex features through an arithmetic-level method. It greatly reduces the feature dimension while keeping the relationship between molecule features. Since the dimension reduction may cause information loss, in the process of complex feature construction, the arithmetic mean strategy is adopted to enhance the sample information further. Finally, three feature ranking methods are integrated for feature selection on constructed complex features. It can adaptively retain important features and remove redundant ones. Extensive experiment results show that RNAI-FRID can provide reliable feature representation for RNA\u2013RNA interaction with higher efficiency and the model trained with generated features obtain better performance than other deep neural network predictors.<\/jats:p>","DOI":"10.1093\/bib\/bbac107","type":"journal-article","created":{"date-parts":[[2022,3,2]],"date-time":"2022-03-02T12:07:56Z","timestamp":1646222876000},"source":"Crossref","is-referenced-by-count":14,"title":["RNAI-FRID: novel feature representation method with information enhancement and dimension reduction for RNA\u2013RNA interaction"],"prefix":"10.1093","volume":"23","author":[{"given":"Qiang","family":"Kang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning, 116024, 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