The talk will provide an introduction to Artificial Intelligence (AI) applications in life sciences. Beginning with fundamental concepts such as regression and classification tasks, it will explain the key metrics used to assess model performance and showcase specific applications in life sciences. The lecture will also address the challenges and limitations of AI models, like data scarcity,...
This talk provides an introduction to omics data, highlighting the different omics data types, such as genomics and proteomics, and explaining experimental methods like the collection of transcriptomics data via RNA sequencing. In addition, it addresses the challenges of collecting omics data and their inherent biases. The aim is to provide a fundamental understanding of omics data...
This talk will give a general overview of data analysis steps commonly preceding the application of actual AI modeling approaches. Possible challenges and solutions in handling real-world data will be illustrated with examples of omics datasets. After highlighting the importance of data representation, normalization, quality control and batch effect correction, the lecture will address how to...
Unfortunately, the lecture by Bastian Riek planned for 23 May has to be rescheduled. We will inform all participants registered for the lecture series by email about a new date.
Diffusion models are a novel class of generative machine learning models that have recently gained significant traction in different fields, including biomedical applications. Such models operate by gradually...
The rapid evolution of transformer-based large language models (LLMs) has revolutionized numerous fields, with their impact on genomics promising to unlock new frontiers in biology and medicine. In this lecture, we will cover basics of language models, explaining how they predict and interpret sequences of symbols, and how we process DNA sequences for computational processing. The core of the...
This lecture provides a comprehensive overview of the ethical landscape surrounding AI models. We will briefly cover the history of AI ethics to understand concepts particularly relevant in the life sciences, such as non-maleficence, bias, fairness, transparency, and responsibility. From there, we will look at life-science-relevant AI cases to discuss the potential real-world implications of...
This lecture introduces drug discovery, emphasising the role of Machine Learning (ML) and Deep Learning (DL) in modern pharmaceutical research. It covers the application of these technologies in predicting drug-target interactions, highlighting their importance in developing new therapies. The session aims to offer a comprehensive overview of the intersection between AI and drug discovery,...
Diffusion models are a novel class of generative machine learning models that have recently gained significant traction in different fields, including biomedical applications. Such models operate by gradually injecting additional noise into data, yielding a distribution of random noise. To generate new unseen instances, the random noise distribution is sampled and subjected to a denoising...
This lecture introduces applications of AI models to omics data in the context of precision medicine. The key goals are 1) to understand the molecular processes underlying complex human phenotypes to be able to rationally device new therapeutic strategies; 2) to identify individuals at high risk of disease and 3) to predict the response to treatment. The lecture will introduce recent work from...
This lecture provides a high-level overview of the capabilities and future perspectives of ChatGPT and GPT-4, focusing on their applications in the scientific world. It provides insights into the workings of large language models (LLMs) and offers practical guidance on how to use ChatGPT as a scientist effectively. Attendees will learn about enhancing their research and writing through...
