Biological Data

Data Learning: Understanding Biological Data

The four most important data-related considerations for the bioinformatic analysis of biological systems are understanding of: the complexity and hierarchical nature of processes that generate biological data, fuzziness of biological data, biases and potential misconceptions in data, and the effects of noise and errors. We discuss these issues and summarize our findings by defining a Data Learn...

Clustering Biological Data

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Big Biological Data Management

With the deluge of omics data, the life sciences have become a big data science. The management and analysis of omics data share many of the challenges and technical solutions of other big data fields. However, there are also unique challenges. In particular, there is a need for data management solutions that are backward compatible with unmodified tools, but at the same time scales to large-sc...

Integration of Biological Data

Biological Laboratory Data

pharmacology, clinical, toxicity, and site of action where known. In some instances, the mode of action of the drug is also given but this is of course a futile section in view of our ignorance on this subject. References are given throughout the discussion of each drug. This handbook is satisfactory for a neophyte in this field; for the physician or for the intensive investigator, the discussi...