Deep machine learning
Driven by the law of Moore, today’s exponential growth in data is about to bring a whole new wave of societal and economic change. It will make cities smarter, societies healthier, and companies more efficient and effective. Given that human brain power is not increasing at an exponential rate, the success of this wave of change depends on advances in the field of (big) data science. This emerging field of study aims at the automatic extraction of actionable knowledge from raw data. For making sense of vast amounts of data, deep neural networks have recently proven to outperform different machine learning techniques. Their usage has even attracted more attention after Google DeepMind’s AlphaGo managed to beat Sedol Lee in a five-game Go match in March 2016. In our research, we explore the application of deep machine learning to different biotech use cases, ranging from genome annotation (e.g., splice site detection in DNA sequences) and functional protein prediction to tumor detection in medical images.