| scholarly article | Q13442814 |
| P2093 | author name string | Michael K Skinner | |
| Lawrence B Holder | |||
| M Muksitul Haque | |||
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| P275 | copyright license | Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International | Q24082749 |
| P433 | issue | 7 | |
| P407 | language of work or name | English | Q1860 |
| P921 | main subject | machine learning | Q2539 |
| regulation of gene expression | Q411391 | ||
| epigenomics | Q3589153 | ||
| genetic epigenesis | Q64443099 | ||
| P304 | page(s) | 505-514 | |
| P577 | publication date | 2017-05-19 | |
| 2017-07-03 | |||
| P13046 | publication type of scholarly work | review article | Q7318358 |
| P1433 | published in | Epigenetics | Q15753739 |
| P1476 | title | Machine learning for epigenetics and future medical applications | |
| P478 | volume | 12 |
| Q47547031 | Artificial Intelligence, Physiological Genomics, and Precision Medicine |
| Q47780833 | Automating drug discovery |
| Q99562144 | Computational methods and next-generation sequencing approaches to analyze epigenetics data: Profiling of methods and applications |
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| Q91972059 | Epigenetic IVD Tests for Personalized Precision Medicine in Cancer |
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| Q57903251 | Machine learning for integrating data in biology and medicine: Principles, practice, and opportunities |
| Q60044953 | Machine learning selected smoking-associated DNA methylation signatures that predict HIV prognosis and mortality |
| Q64229588 | Making Sense of the Epigenome Using Data Integration Approaches |
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