1. A device for selecting genes or proteins relevant to a specific function from a set of candidate genes or proteins without supervised machine learning or positive and negative examples, the device comprising: a storage device that stores a data about a collection of genes or proteins, with which annotations are associated; an input device that receives an input of the set of candidate genes or proteins; and a processor that: (a) gathers annotations that are associated with the candidate genes or proteins inputted, the annotations being gathered from the storage device; (b) chooses annotations that are associated with the candidate genes or proteins more than a threshold number of times or frequencies, the annotations being chosen from the annotations gathered, wherein the threshold number of times or frequencies are selected from the group consisting of: (i) a threshold number of times or frequencies determined by the processor, wherein the processor determines the threshold number of times or frequencies so that the number of times or frequencies the annotation is associated with the candidate genes or proteins is larger than a number of times or frequencies the annotation is associated with control genes or proteins; and (ii) a threshold number of times or frequencies determined by the processor, wherein the processor determines the threshold number of times or frequencies so that the number of times or frequencies the annotation is associated with the candidate genes or proteins is larger than a number of times or frequencies the annotation is associated with control genes or proteins with statistical significance, and a p-value provided by a statistical significance test comparing the number of times or frequencies the annotation is associated with the candidate genes or proteins to the number of times or frequencies the annotation is associated with the control genes or proteins is less than a predetermined value; and (c) selects genes or proteins, with which at least one of the chosen annotations is associated, the genes or proteins being selected from the set of candidate genes or proteins inputted without supervised machine learning or positive and negative examples, wherein the selected genes or proteins are considered having relevancy to the specific function from the set of candidate genes or proteins.