Diseases like flu and chickenpox are relatively easy to diagnose. It is known that symptoms like fever, aching limbs, a cough or a characteristic skin rash can be traced back to a specific pathogen. But it is much more difficult to diagnose genetic diseases at an early stage – partly because a gene mutation can lead to symptoms of varying severity, but also because their clinical manifestations often differ. In the past, some were not linked to genetic variations at all, which meant hereditary diseases remained unidentified. Even today, people with these diseases must often make numerous visits to an array of specialists until they receive an accurate diagnosis of their condition.
HPO: a computational resource for diagnosing rare hereditary diseases
In 2008, the medical researcher and genome expert Peter N. Robinson developed the Human Phenotype Ontology (HPO), a standardized vocabulary of phenotypic abnormalities encountered in human disease. The database allows users to match clinical manifestations of diseases with relevant gene mutations and syndromes. This computational resource is continuously updated and currently contains over 13,000 terms and 156,000 annotations to hereditary diseases. Users can employ various applications to search through the data and weight findings based on the information they provide – thus facilitating clinical diagnostics.
With the creation of this database, Robinson has laid the foundation for using artificial intelligence to identify and track rare hereditary diseases. He is a pioneer in computational phenotype analysis and has been one of the top researchers in the field for many years. He himself has discovered several hereditary diseases associated with single genes. He has also written algorithms that assist in investigating genome and exome sequences and in establishing correlations between clinical manifestations and gene mutations. The next step is to bridge the gap between data science and applied medicine.
Leveraging big data for personalized precision medicine
Closing this gap is precisely what Robinson seeks to do at the BIH. “We want to combine clinical and genomic information from large cohorts and use this wealth of data to categorize patients into subgroups who, due to similar molecular pathways, have a similar disease progression and may therefore respond to the same treatment,” says Robinson. “In the clinic, patients categorized in this way could be offered the most effective treatment for their specific condition, which would be an important step toward personalized precision medicine.” Robinson also wants to use his expertise in computer modeling and clinical and phenotypic data analysis to develop and expand decision support software that physicians can use to diagnose and treat patients.
“In Peter N. Robinson, the Berlin Institute of Health at Charité (BIH) is gaining an excellent protagonist in translational bioinformatics research,” says Prof. Christopher Baum, Scientific Director of the BIH and Chief Translational Research Officer of Charité – Universitätsmedizin Berlin. “He will have a major impact on the field of digital medicine at the BIH and beyond, and through his exceptional scientific and medical career, he will be a role model for the next generation of clinical researchers. His approach is translational through and through and is typified by strong international networks. By harnessing bioinformatics and data science, Robinson is developing resources that help guide physicians’ treatment decisions in the clinic and pave the way for patients to receive customized treatments. That makes him a perfect fit for the BIH and our motto, ‘Turning research into health.’”
From Charité to the USA and back again
Peter N. Robinson studied mathematics and computer science at Columbia University, New York City, USA, and medicine at the University of Pennsylvania, Philadelphia, USA. In 2000, he completed his training in pediatrics at Charité – Universitätsmedizin Berlin. During his first stint at Charité, he held a professorship in medical genomics at the Institute of Medical Genetics and Human Genetics, while also serving as a fellow at the Berlin-based Max Planck Institute for Molecular Genetics and a co-opted professor at the Institute for Bioinformatics at Freie Universität Berlin. In 2016, he moved to The Jackson Laboratory for Genomic Medicine in Farmington, Connecticut, USA, where he is Professor of Computational Biology.
Humboldt Professorships for excellent researchers
The Alexander von Humboldt Professorship is the most highly-endowed research award in Germany and draws top international researchers to German universities. The Humboldt Professorship is funded by the Federal Ministry of Education and Research. It enables the holder to conduct long-term, forward-looking research at universities and research institutions in this country and makes a sustainable contribution to Germany’s ability to compete internationally as a location for research. Since 2008, up to ten Humboldt Professorships have been granted every year as part of the International Research Fund for Germany. In 2020, additional Alexander von Humboldt Professorships for Artificial Intelligence (AI) were created.