Because different types of biological data usually provide complementary information regarding the underlying GRN, a model that integrates big data of diverse types is expected to increase both the power and accuracy of GRN inference.
The enormous challenge in this era of big data is making biological sense of different levels of data, including the transcriptome, proteome, metabolome and clinical chemistry data.
These domains are transportation (self-driving cars and the city of the future), home/service robots (vacuum cleaners), health care (big data based analysis of biological processes), education (the automation of instruction), low resource communities (gaining access to the internet), public safety (surveillance), employment (automation of work processes), and entertainment (internet of things).
In the era of big data, human-related biological databases continue to grow not only in count but also in volume, posing unprecedented challenges in data storage, processing, exchange, and curation.
First of all, the measurement model of established big data-driven biological embedded engineering was proposed based on the research of biological engineering with non-linear and unpredictability.
"Section 2" describes the big data-driven biological embedded engineering measurement model.
To date, several approaches overcome the handling of big data masses to extract biological meaning.
In the era of big data challenges, as the availability of biological information becomes ever larger and more complex, more computational tools are needed to sort through the data.
We highlight the novel findings in the application of functional connectomic big data to the exploration of the biological mechanisms of cognitive functions, normal development and aging and of neurological and psychiatric disorders.
