Data issues in the life sciences

Data issues in the life sciences

We review technical and sociological issues facing the Life Sciences as they transform into more data-centric disciplines - the "Big New Biology". Three major challenges are: 1) lack of comprehensive standards; 2) lack of incentives for individual scientists to share data; 3) lack of appropriate infrastructure and support. Technological advances with standards, bandwidth, distributed computing,...

Data clustering in life sciences.

Clustering has a wide range of applications in life sciences and over the years has been used in many areas ranging from the analysis of clinical information, phylogeny, genomics, and proteomics. The primary goal of this article is to provide an overview of the various issues involved in clustering large biological datasets, describe the merits and underlying assumptions of some of the commonly...

Tackling Big Data in the Life Sciences

classes that group together proteins with similar amino-acid subsequences and similar biological properties. We instigated Kbdock (Kb for “knowledge-based”) to address the problem of PPI modelling and classification at the domain level. In fact, as in many other complex scientific domains, big data approaches in the life sciences can only become viable by explicitly considering and making use o...

Linked Data for Life Sciences

Massive amounts of data are currently available and being produced at an unprecedented rate in all domains of life sciences worldwide. However, this data is disparately stored and is in different and unstructured formats making it very hard to integrate. In this review, we examine the state of the art and propose the use of the Linked Data (LD) paradigm, which is a set of best practices for pub...

Exploring Life Sciences Data Sources