Drug Res (Stuttg) 2018; 68(06): 305-310
DOI: 10.1055/s-0043-124761
Opinion Paper
© Georg Thieme Verlag KG Stuttgart · New York

Developing Deep Learning Applications for Life Science and Pharma Industry

Daniel Siegismund
1  Genedata AG, Basel, Switzerland
,
Vasily Tolkachev
2  Institute of Data Analysis and Process Design, Winterthur, Switzerland
,
Stephan Heyse
1  Genedata AG, Basel, Switzerland
,
Beate Sick
2  Institute of Data Analysis and Process Design, Winterthur, Switzerland
,
Oliver Duerr
2  Institute of Data Analysis and Process Design, Winterthur, Switzerland
,
Stephan Steigele
1  Genedata AG, Basel, Switzerland
› Author Affiliations
Further Information

Publication History

received 20 June 2017

accepted 08 December 2017

Publication Date:
16 January 2018 (eFirst)

Abstract

Deep Learning has boosted artificial intelligence over the past 5 years and is seen now as one of the major technological innovation areas, predicted to replace lots of repetitive, but complex tasks of human labor within the next decade. It is also expected to be ‘game changing’ for research activities in pharma and life sciences, where large sets of similar yet complex data samples are systematically analyzed. Deep learning is currently conquering formerly expert domains especially in areas requiring perception, previously not amenable to standard machine learning. A typical example is the automated analysis of images which are typically produced en-masse in many domains, e. g., in high-content screening or digital pathology. Deep learning enables to create competitive applications in so-far defined core domains of ‘human intelligence’. Applications of artificial intelligence have been enabled in recent years by (i) the massive availability of data samples, collected in pharma driven drug programs (=‘big data’) as well as (ii) deep learning algorithmic advancements and (iii) increase in compute power. Such applications are based on software frameworks with specific strengths and weaknesses. Here, we introduce typical applications and underlying frameworks for deep learning with a set of practical criteria for developing production ready solutions in life science and pharma research. Based on our own experience in successfully developing deep learning applications we provide suggestions and a baseline for selecting the most suited frameworks for a future-proof and cost-effective development.