Our approach is not anchored by traditional bibliometric analysis.  ‘Published’ does not mean ‘True’ not even ‘Relevant’. We make trends identify and defend themselves before we spend the resources to investigate. 

We start from First Principles. We scan the scientific literature as if it was the research subject. We analyze the words, hypothesis, and conclusions to see what connections exist. Then, we draw out the connections between the candidate topics. We look at everything and focus on the emerging. 

Trend & Evolution

New knowledge is created when trends take hold. Emerging topics that are worthy of further consideration attract support from possibly unconnected researchers.  We show how individual ideas evolve and how they relate to other emerging topics.

Concept Identification 

We identify trends that will emerge in the next 3 to 5 years. We are not chasing the Next Big Thing. We find the Next Things that have the potential to be Big. Then we back that up by tracing the concepts, researchers, and companies that are bringing it to life. 

Use Case: Proteomics

The trends of the last 20 years in biotechnology are revealed using artificial intelligence and natural language processing (NLP) of publicly available data. Implementing this “science-of-science” approach, we capture convergent trends in the field of proteomics in both technology development and application across the phylogenetic tree of life. With major gaps in our knowledge about protein composition, structure, and location over time, we report trends in persistent, popular approaches and emerging technologies across 94 ideas from a corpus of 29 journals in PubMed over two decades. New metrics for clusters of these ideas reveal the progression and popularity of emerging approaches like single-cell, spatial, compositional, and chemical proteomics designed to better capture protein-level chemistry and biology. This analysis of the proteomics literature with advanced analytic tools quantifies the Rate of Rise for a next generation of technologies to better define, quantify, and visualize the multiple dimensions of the proteome that will transform our ability to measure and understand proteins in the coming decade.

Exploiting data and technology at scale



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