# An unsupervised machine learning method for discovering patient clusters based on genetic signatures

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/an-unsupervised-machine-learning-method-for-discovering-patient-clusters-based/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Christian López,Scott Tucker,T. Salameh,Conrad S. Tucker |
| Citations | 135 |
| DOI | 10.1016/j.jbi.2018.07.004 |
| Fields | Biochemistry, Genetics and Molecular Biology |
| Open Access | true |
| OA Status | bronze |
| OA URL | https://www.sciencedirect.com/science/article/pii/S1532046418301308/pdf |
| OpenAlex ID | https://openalex.org/W2884205346 |
| PMID | 30016722 |
| Type | article |
| Year | 2018 |

## Paper authors

- [Conrad S. Tucker](https://scholariq.org/researchers/conrad-s-tucker/)

## Paper journal

- [Journal of Biomedical Informatics](https://scholariq.org/journals/journal-of-biomedical-informatics/)

## Paper primary topic

- [Bioinformatics and Genomic Networks](https://scholariq.org/topics/bioinformatics-and-genomic-networks/)

## Paper topics

- [Bioinformatics and Genomic Networks](https://scholariq.org/topics/bioinformatics-and-genomic-networks/)
- [Gene expression and cancer classification](https://scholariq.org/topics/gene-expression-and-cancer-classification/)
- [vaccines and immunoinformatics approaches](https://scholariq.org/topics/vaccines-and-immunoinformatics-approaches/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.