Search

Text mining applications can help navigate large quantities of text within a range of different biomedical settings, including hospitals, academic laboratories, government safety and regulatory agencies, and pharmaceutical research and development. The development of text mining applications, however, is highly dependent on the availability of sources of textual content and the capabilities of current text mining algorithms. The performance of such algorithms can be highly variable depending on the tasks that need to be addressed. Within such constraints, text mining applications can be deployed for diverse purposes, such as semi-automated curation of biological databases, pharmacovigilance, biomarker discovery, construction of signaling pathways and prediction of protein function and similarity amongst others. As the availability of and need for analyzing large sources of text grows in biomedicine, text mining applications will occupy an increasingly more important role.

Combinatorial network optimization theory concerns minimization of connection costs among interconnected components in systems such as electronic circuits. Similar wiring minimization can be observed at various levels of nervous systems, invertebrate and vertebrate, from placement of the entire brain in the body down to the subcellular level of neuron arbor geometry. In some cases, the minimization appears to be perfect or as good as can be detected with current methods. One question such “best of all possible brains” results raises is, what is the function of optimizing, rather than just reducing, these neural connection costs?