Title |
Machine learning and systems genomics approaches for multi-omics data
|
---|---|
Published in |
Biomarker Research, January 2017
|
DOI | 10.1186/s40364-017-0082-y |
Pubmed ID | |
Authors |
Eugene Lin, Hsien-Yuan Lane |
Abstract |
In light of recent advances in biomedical computing, big data science, and precision medicine, there is a mammoth demand for establishing algorithms in machine learning and systems genomics (MLSG), together with multi-omics data, to weigh probable phenotype-genotype relationships. Software frameworks in MLSG are extensively employed to analyze hundreds of thousands of multi-omics data by high-throughput technologies. In this study, we reviewed the MLSG software frameworks and future directions with respect to multi-omics data analysis and integration. Our review was targeted at researching recent approaches and technical solutions for the MLSG software frameworks using multi-omics platforms. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 7 | 41% |
Colombia | 1 | 6% |
Sweden | 1 | 6% |
Chile | 1 | 6% |
Germany | 1 | 6% |
Iran, Islamic Republic of | 1 | 6% |
United Kingdom | 1 | 6% |
Unknown | 4 | 24% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 12 | 71% |
Scientists | 2 | 12% |
Practitioners (doctors, other healthcare professionals) | 2 | 12% |
Science communicators (journalists, bloggers, editors) | 1 | 6% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 1 | <1% |
Unknown | 379 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 77 | 20% |
Student > Ph. D. Student | 72 | 19% |
Student > Master | 47 | 12% |
Student > Bachelor | 30 | 8% |
Student > Doctoral Student | 22 | 6% |
Other | 57 | 15% |
Unknown | 75 | 20% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 84 | 22% |
Agricultural and Biological Sciences | 55 | 14% |
Computer Science | 54 | 14% |
Medicine and Dentistry | 23 | 6% |
Engineering | 21 | 6% |
Other | 58 | 15% |
Unknown | 85 | 22% |