Computational and Experimental Identification of Tissue-Specific MicroRNA Targets

Raheleh Amirkhah, Hojjat Naderi Meshkin, Ali Farazmand, John E J Rasko, Ulf Schmitz

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


In this chapter we discuss computational methods for the prediction of microRNA (miRNA) targets. More specifically, we consider machine learning-based approaches and explain why these methods have been relatively unsuccessful in reducing the number of false positive predictions. Further we suggest approaches designed to improve their performance by considering tissue-specific target regulation. We argue that the miRNA targetome differs depending on the tissue type and introduce a novel algorithm that predicts miRNA targets specifically for colorectal cancer. We discuss features of miRNAs and target sites that affect target recognition, and how next-generation sequencing data can support the identification of novel miRNAs, differentially expressed miRNAs and their tissue-specific mRNA targets. In addition, we introduce some experimental approaches for the validation of miRNA targets as well as web-based resources sharing predicted and validated miRNA target interactions.

Original languageEnglish
Pages (from-to)127-147
JournalMethods in Molecular Biology
Publication statusPublished - 25 Apr 2017
Externally publishedYes


  • Animals
  • Colorectal Neoplasms/genetics
  • Gene Expression Regulation
  • Genomics/methods
  • High-Throughput Nucleotide Sequencing/methods
  • Humans
  • Immunoprecipitation/methods
  • Machine Learning
  • MicroRNAs/genetics
  • RNA, Messenger/genetics
  • Sequence Analysis, RNA/methods
  • Software


Dive into the research topics of 'Computational and Experimental Identification of Tissue-Specific MicroRNA Targets'. Together they form a unique fingerprint.

Cite this