Abstract
MicroRNAs (miRNAs) are 19 to 25 nucleotides non-coding RNAs known to possess important posttranscriptional regulatory functions. Identifying targeting genes that miRNAs regulate are important for understanding their specific biological functions. Usually, miRNAs down-regulate target genes through binding to the complementary sites in the 3 untranslated region (UTR) of the targets. In part, due to the large number of miRNAs and potential targets, an experimental based prediction design would be extremely laborious and economically unfavorable. However, since the bindings of the animal miRNAs are not a perfect one-to-one match with the complementary sites of their targets, it is difficult to predict targets of animal miRNAs by accessing their alignment to the 3 UTRs of potential targets. Consequently, sophisticated computational approaches for miRNA target prediction are being considered as essential methods in miRNA research. We surveyed most of the current computational miRNA target prediction algorithms in this paper. Particularly, we provided a mathematical definition and formulated the problem of target prediction under the framework of statistical classification. Moreover, we summarized the features of miRNA-target pairs in target prediction approaches and discussed these approaches according to two categories, which are the rule-based and the data-driven approaches. The rule-based approach derives the classifier mainly on biological prior knowledge and important observations from biological experiments, whereas the data driven approach builds statistic models using the training data and makes predictions based on the models. Finally, we tested a few different algorithms on a set of experimentally validated true miRNA-target pairs [1] and a set of false miRNA-target pairs, derived from miRNA overexpression experiment [2]. Receiver Operating Characteristic (ROC) curves were drawn to show the performances of these algorithms.
Current Genomics
Title: Survey of Computational Algorithms for MicroRNA Target Prediction
Volume: 10 Issue: 7
Author(s): Dong Yue, Hui Liu and Yufei Huang
Affiliation:
Abstract: MicroRNAs (miRNAs) are 19 to 25 nucleotides non-coding RNAs known to possess important posttranscriptional regulatory functions. Identifying targeting genes that miRNAs regulate are important for understanding their specific biological functions. Usually, miRNAs down-regulate target genes through binding to the complementary sites in the 3 untranslated region (UTR) of the targets. In part, due to the large number of miRNAs and potential targets, an experimental based prediction design would be extremely laborious and economically unfavorable. However, since the bindings of the animal miRNAs are not a perfect one-to-one match with the complementary sites of their targets, it is difficult to predict targets of animal miRNAs by accessing their alignment to the 3 UTRs of potential targets. Consequently, sophisticated computational approaches for miRNA target prediction are being considered as essential methods in miRNA research. We surveyed most of the current computational miRNA target prediction algorithms in this paper. Particularly, we provided a mathematical definition and formulated the problem of target prediction under the framework of statistical classification. Moreover, we summarized the features of miRNA-target pairs in target prediction approaches and discussed these approaches according to two categories, which are the rule-based and the data-driven approaches. The rule-based approach derives the classifier mainly on biological prior knowledge and important observations from biological experiments, whereas the data driven approach builds statistic models using the training data and makes predictions based on the models. Finally, we tested a few different algorithms on a set of experimentally validated true miRNA-target pairs [1] and a set of false miRNA-target pairs, derived from miRNA overexpression experiment [2]. Receiver Operating Characteristic (ROC) curves were drawn to show the performances of these algorithms.
Export Options
About this article
Cite this article as:
Yue Dong, Liu Hui and Huang Yufei, Survey of Computational Algorithms for MicroRNA Target Prediction, Current Genomics 2009; 10 (7) . https://dx.doi.org/10.2174/138920209789208219
DOI https://dx.doi.org/10.2174/138920209789208219 |
Print ISSN 1389-2029 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5488 |
Call for Papers in Thematic Issues
Advanced AI Techniques in Big Genomic Data Analysis
The thematic issue on "Advanced AI Techniques in Big Genomic Data Analysis" aims to explore the cutting-edge methodologies and applications of artificial intelligence (AI) in the realm of genomic research, where vast amounts of data pose both challenges and opportunities. This issue will cover a broad spectrum of AI-driven strategies, ...read more
Advanced Computational Algorithms and Artificial Intelligence in Clinical Pharmacogenomics
In the era of personalized medicine, understanding the relationship between genetics and drug response is crucial. This issue delves into innovative methodologies, leveraging deep computational analysis and artificial intelligence, to enhance the field of Clinical Pharmacogenomics. The interdisciplinary approach harnesses the power of advanced high-throughput genotyping technologies, sophisticated computational analysis, ...read more
Applications of Single-cell Sequencing Technology in Reproductive Medicine
Single cell sequencing (SCS) technology utilizes individual cells' genetic material to sequence their genome, transcriptome, and epigenetics at the molecular level. It offers insights into cell heterogeneity and enables the study of limited biological materials. Since its recognition as a valuable technique in 2011, single cell sequencing has yielded numerous ...read more
Big Data in Cancer Research
Cancer is a significant threat to human life and health, remaining a highly aggressive killer. It is a leading cause of death worldwide and represents a crucial medical issue for humanity. However, in the past decade, the effectiveness of new synthetic anticancer agents has not matched the current clinical speculation. ...read more
Related Journals
- Author Guidelines
- Graphical Abstracts
- Fabricating and Stating False Information
- Research Misconduct
- Post Publication Discussions and Corrections
- Publishing Ethics and Rectitude
- Increase Visibility of Your Article
- Archiving Policies
- Peer Review Workflow
- Order Your Article Before Print
- Promote Your Article
- Manuscript Transfer Facility
- Editorial Policies
- Allegations from Whistleblowers
- Announcements
Related Articles
-
Knemometry for Assessment of Growth Suppressive Effects of Exogenous Glucocorticoids
Current Pediatric Reviews The Urokinase-type Plasminogen Activator and the Generation of Inhibitors of Urokinase Activity and Signaling
Current Pharmaceutical Design New Anticancer Drugs from Marine Cyanobacteria
Current Drug Targets NF-κB Inhibitors in Head and Neck Cancer
Letters in Drug Design & Discovery Clinical Experience with Thalidomide and Lenalidomide in Multiple Myeloma
Current Cancer Drug Targets Commercial Availability of Alpha-Emitting Radionuclides for Medicine
Current Radiopharmaceuticals Targeting the Ubiquitin Proteasome System: Beyond Proteasome Inhibition
Current Pharmaceutical Design A novel NGR-conjugated peptide targets DNA damage responses for radiosensitization
Current Cancer Drug Targets Helminth Infections and Cardiovascular Diseases: Toxocara Species is Contributing to the Disease
Current Cardiology Reviews Naphthalimides and Azonafides as Promising Anti-Cancer Agents
Current Medicinal Chemistry Current Development of ROS-Modulating Agents as Novel Antitumor Therapy
Current Cancer Drug Targets CX-4945, a Selective Inhibitor of Casein Kinase 2, Synergizes with B Cell Receptor Signaling Inhibitors in Inducing Diffuse Large B Cell Lymphoma Cell Death
Current Cancer Drug Targets Microtubule Targeting Agents: A Benchmark in Cancer Therapy
Current Drug Therapy Finding Drug Targets Through Analysis of the Platelet Transcriptome
Current Pharmaceutical Design Anti-tumor Drug Targets Analysis: Current Insight and Future Prospect
Current Drug Targets Human Whey Promotes Sessile Bacterial Growth, Whereas Alternative Sources of Infant Nutrition Promote Planktonic Growth
Current Nutrition & Food Science Liposomes Containing Gadodiamide: Preparation, Physicochemical Characterization, and In Vitro Cytotoxic Evaluation
Current Drug Delivery The Chlorophyll Catabolite Pheophorbide a as a Photosensitizer for the Photodynamic Therapy
Current Medicinal Chemistry Luteolin, a Flavonoid with Potential for Cancer Prevention and Therapy
Current Cancer Drug Targets Patent Selections
Recent Patents on Anti-Cancer Drug Discovery