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Recent advances in predicting lncRNA–disease associations based on computational methods by Jing Yan; Ruobing Wang; Jianjun Tan is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.
What is Recent advances in predicting lncRNA–disease associations based on computational methods about?
Mutations in and dysregulation of long non-coding RNAs (lncRNAs) are closely associated with the development of various human complex diseases, but only a few lncRNAs have been experimentally confirmed to be associated with human diseases. Predicting new potential lncRNA–disease associations (LDAs) will help us to understand the pathogenesis of human diseases and to detect disease markers, as well as in disease diagnosis, prevention and treatment. Computational methods can effectively narrow down the screening scope of biological experiments, thereby reducing the duration and cost of such experiments. In this review, we outline recent advances in computational methods for predicting LDAs, focusing on LDA databases, lncRNA/disease similarity calculations, and advanced computational models. In addition, we analyze the limitations of various computational models and discuss future challenges and directions for development.
Who reads Recent advances in predicting lncRNA–disease associations based on computational methods?
It is typically read by researchers, students, and practitioners in Biochemistry, Genetics and Molecular Biology.
- Author
- Jing Yan; Ruobing Wang; Jianjun Tan
- Publisher
- Elsevier BV
- Published
- 2023
- Language
- EN
- Field
- Biochemistry, Genetics and Molecular Biology (Life Sciences)