MICHELE LEONARDI
Skin Research Institute, Oriflame Cosmetic AB, Stockholm, Sweden

Abstract

Active natural products have long been endowed with special properties and have made undeniable contributions to the development of the pharma and beauty industries and research. New high-throughput analytical, biological and pharmacological techniques applied to the natural products have generated in the last years a large volume of data. Therefore, despite their usefulness, the complete translation from natural sources to the end active products remain a challenge for the industry and research. To avoid this challenge, a new approach based on the emerging “big data” concept and network analysis, is required. In this work, the generation of bipartite “singular natural compound-target” networks are presented, as key translational point from natural sources and biological activity.

INTRODUCTION

The “Big Data” concept plays an increasingly important role in every scientific fields nowadays. It´s affecting many area of life, more so than we might realize (1, 2). One of the area where the big data is beginning to emerge and provide new opportunities, because of their application in medicine (3-5), are the Pharmacognosy and Natural Compounds (NCs) fields. What is “Big Data”? In 2001, analyst D Laney (6) characterized the “Big Data” concept with the necessity to control the Volume, Variety and Velocity of Data generated. Based on the Laney´s assumption the large international firm Gartner Inc. (6)
for the first time, put forward the general definition of “Big Data” concept: “Big Data is high-volume, high velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.” Based on this definition and the Laney´s assumption Volume, Velocity and Variety became the often cited “3Vs” of Big Data concept. The fundamental big data characterization of Laney has been further enlarged over the ...