| Chapter title |
Machine Learning Techniques in Exploring MicroRNA Gene Discovery, Targets, and Functions
|
|---|---|
| Chapter number | 16 |
| Book title |
Bioinformatics in MicroRNA Research
|
| Published in |
Methods in molecular biology, May 2017
|
| DOI | 10.1007/978-1-4939-7046-9_16 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-7044-5, 978-1-4939-7046-9
|
| Authors |
Singh, Sumi, Benton, Ryan G., Singh, Anurag, Singh, Anshuman, Sumi Singh, Ryan G. Benton Ph.D., Anurag Singh, Anshuman Singh, Ryan G. Benton |
| Editors |
Jingshan Huang, Glen M. Borchert, Dejing Dou, Jun (Luke) Huan, Wenjun Lan, Ming Tan, Bin Wu |
| Abstract |
In recent years, the role of miRNAs in post-transcriptional gene regulation has provided new insights into the understanding of several types of cancers and neurological disorders. Although miRNA research has gathered great momentum since its discovery, traditional biological methods for finding miRNA genes and targets continue to remain a huge challenge due to the laborious tasks and extensive time involved. Fortunately, advances in computational methods have yielded considerable improvements in miRNA studies. This literature review briefly discusses recent machine learning-based techniques applied in the discovery of miRNAs, prediction of miRNA targets, and inference of miRNA functions. We also discuss the limitations of how these approaches have been elucidated in previous studies. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 10 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 2 | 20% |
| Student > Ph. D. Student | 2 | 20% |
| Professor | 1 | 10% |
| Other | 1 | 10% |
| Student > Master | 1 | 10% |
| Other | 1 | 10% |
| Unknown | 2 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 2 | 20% |
| Chemical Engineering | 1 | 10% |
| Nursing and Health Professions | 1 | 10% |
| Agricultural and Biological Sciences | 1 | 10% |
| Computer Science | 1 | 10% |
| Other | 2 | 20% |
| Unknown | 2 | 20% |