Tobias Kuhn

2025-05-20T09:46:02.734Z

Tobias Kuhn

2025-05-20T09:46:02.734Z

Valorizing omics visualization for discovery

Tobias Kuhn

2025-05-20T09:46:02.734Z

2017-10-17

Tobias Kuhn

2025-05-20T09:46:02.734Z

Scientists from diverse backgrounds are joining the field of data science. This leads to advances in data science being actualized in the context of many different domains. Conclusions from datasets using innovative algorithms are obvious aspects but advances in data science can take on many different forms such as new methods for data interpretation, new data integration and processing technologies, or as will be the topic of this editorial, data visualization techniques. The parity and complementary relationship between techniques from all domains provide ways to improve discovery although quantifying the contributions to discovery process from each technique can be elusive. The experiences described here come from visualizing life science multi-omics data, but most of the remarks can be associated with visualization methods in general. From the perspective that visualization serves as an important method for shaping data science interpretations, this paper sets out: 1) some of the necessary requirements for visualization tools due to the nature of multi-omics datasets and, 2) some of the difficulties encountered in creating and valorizing new visualization implementations for scientific discovery.

Tobias Kuhn

2025-05-20T09:46:02.734Z

1-2

Tobias Kuhn

2025-05-20T09:46:02.734Z

1

Tobias Kuhn

2025-05-20T09:46:02.734Z

Tobias Kuhn

2025-05-20T09:46:02.734Z