Siegismund Daniel, Tolkachev Vasily, Heyse Stephan, Sick Beate, Duerr Oliver, Steigele Stephan
Genedata AG, Basel, Switzerland.
Institute of Data Analysis and Process Design, Winterthur, Switzerland.
Drug Res (Stuttg). 2018 Jun;68(6):305-310. doi: 10.1055/s-0043-124761. Epub 2018 Jan 16.
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.
在过去五年中，深度学习推动了人工智能的发展，如今它被视为主要的技术创新领域之一，预计在未来十年内将取代许多重复性但复杂的人类劳动任务。对于制药和生命科学领域的研究活动而言，深度学习也有望带来“变革”，因为在这些领域中，大量相似但复杂的数据样本需要进行系统分析。深度学习目前正在攻克以前由专家主导的领域，尤其是在需要感知的领域，这些领域以前并不适合标准的机器学习。一个典型的例子是图像的自动分析，在许多领域，如高内涵筛选或数字病理学中，图像通常是大量生成的。深度学习能够在迄今为止定义的“人类智能”核心领域中创建具有竞争力的应用程序。近年来，人工智能的应用得益于以下几点：（i）在制药驱动的药物项目中收集的大量数据样本（即“大数据”）；（ii）深度学习算法的进步；（iii）计算能力的提高。此类应用基于具有特定优缺点的软件框架。在此，我们介绍深度学习的典型应用和基础框架，并给出一套用于在生命科学和制药研究中开发可投入生产的解决方案的实用标准。基于我们在成功开发深度学习应用方面的经验，我们提供了一些建议和基线，以选择最适合的框架，实现面向未来且具有成本效益的开发。
