Pico-nym cloud (PNC): A method to devise and peruse semantically related biological patterns

Abstract

Text mining works widely in the field of research techniques, which allow an individual to store text and its important terms in form of electronic document (.doc,.txt). Obliviously, one cannot remember such huge amount of text; moreover, the manual approach is more time-taking, unreliable, and accessible to that person only. Text mining techniques optimize this approach by extracting and storing this data. Computational comparison, file read, file write are more efficiently done. With the help of Pico-Nym Cloud (PNC), we generated more semantically similar, related, and significant patterns. The give, generate, and get sequence modeling is adopted. Over the other available Web applications, we present our application with improved stemming, relation, and average case consideration. This approach does not limit the displayed number of words as all the generated sets can be traversed with the GUI, with opted size of patterns. This PNC is highly applicable in bioinformatics, related information retrieval from document, sentimental analysis using social Web sites (Twitter and Facebook), query expansion (Google) and many more.