We pursue our dictionary-learning-based approach for biological data, based on the following two motivations.
The increasing amount of information stored in publicly available biological data bases is retrieved to find approximate solutions relating sequence to protein structure.
A better, yet more challenging approach is to reconstruct volume meshes from biological data based on closed surface meshes.
In the following text, we describe several aspects of big biological data based on our recent studies.
Many scientists assert that only biological data based on measurements of the variation in human metabolic responses to arsenic will resolve the low-dose controversy.
Systematic comparison among TR finding tools and algorithms operating ab initio, that is without support of specific biological data bases has been tackled in recent years (Leclercq et al., 2007; Saha et al., 2008).
While the Pliocene 'Great American Interchange' between continents approximately 3 MYA considerably affected mammalian fauna [ 25] and some avian fauna [ 27, 28], a growing list of biological data based on molecular phylogenetics also supports an earlier, Miocene, land connection: e.g. frogs [ 29, 30], plants [ 31], procyonid mammals [ 32], and snakes [ 33].
However, most clinical and biological data are based on younger patient cohorts, mostly from clinical studies including highly selected patients.
InterMine not only comes with a number of 'core' biological data models based on SO terms and relationships that are indispensable, e.g. chromosomes, genes and proteins, but it also has the flexibility to include any other SO terms such as histone binding sites, introns and ncRNA as needed.
In this paper, we combined 37 features representing 10 distinct groups of biological data sources based on former studies [ 24, 36, 37], including gene expression, physical interactions, domain interactions, HMS_PCI mass, TAP mass, yeast two-hybrid, genetic interactions, gene ontology (GO) annotations, and gene context analysis, to predict the more reliable protein-protein interactions.
