We consider a data analytics algorithm to mitigate some of these challenges in this work.
In order to implement this idea, several key techniques are illustrated in detail, e.g. data attribute, data cleansing, data integration of databases in different levels, and big data analytic algorithms.
We use the terms predictions, computational inferences or hypotheses to refer to the outcomes of data-analytic algorithms.
In short, data-analytic algorithms analyze big-data and generate hypotheses; in contrast, the proposed inference-analytic algorithms analyze these hypotheses and rank them by predicted biological impact.
For these reasons validating or interpreting all the hundreds of computational inferences of data-analytic algorithms is expensive and is not amenable to crowdsourcing.
In this era of scalable data-analytic algorithms which process large data, producing hundreds of inferences, there needs to be a systematic way of determining which of these hundreds of inferences are to be further studied.
In domains such as biology where hypothesis verification and interpretation are resource-intensive, the large number of inferences drawn by data-analytic algorithms would have to be reanalyzed by various criteria such as availability of resources (budget, reagents or scientific expertise).
The company chose Linkedin for its expertise in data analytics and algorithms, which has helped LinkedIn become far more than a site to post a resume.
