The use of existing and new ontologies in African biomedical research
Prof Nicola Mulder
African scientists have joined the genomics revolution, and large-scale projects to study the genetic and environmental determinants of diseases are underway across the continent. These have been made possible through initiatives such as the Human Heredity and Health in Africa (H3Africa), which is funding research projects, large collaborative centres, bioepositories and a bioinformatics network. H3ABioNet is the NIH funded bioinformatics network, composed of 27 institutions across 16 African countries, which is developing capacity for genomics research. One of the roles of the network is to facilitate data harmonization and submission to public repositories, such as the EGA. Data from the H3Africa projects is being collected for over 100,000 participants on multiple communicable and non-communicable diseases, and includes phenotype, genomic and other kinds of experimental data. H3ABioNet is working with the projects to harmonize the data and map to ontologies. In doing so, we identified a few gaps in existing ontologies, including those for the description of ethnolinguistic groups and for African specific diseases such as Sickle Cell Disease. To address these we extended existing terms for ethnolinguistic groups and developed a Sickle Cell Disease Ontology. This talk will describe some of this work and other challenges encountered in harmonizing the data.
Bio: Prof Mulder heads the Computational Biology Division at the University of Cape Town (UCT), and leads H3ABioNet, a large Pan African Bioinformatics Network of 27 institutions in 16 African countries. H3ABioNet aims to develop bioinformatics capacity to enable genomic data analysis on the continent by developing and providing access to skills and computing infrastructure for data analysis. Prior to her position at UCT, she worked for nearly 9 years at the European Bioinformatics Institute (EBI) in Cambridge, as Team Leader for InterPro and the Gene Ontology Annotation project. At UCT her research focuses on genetic determinants of susceptibility to disease, African genome variation, microbiomes, microbial genomics and infectious diseases from both the host and pathogen perspectives. Her group also provides bioinformatics services for the local researchers, through which they develop visualization and analysis tools for high-throughput biology. Her team has also been involved in the development of new and improved algorithms for the analysis of complex African genetic data as well as for downstream analysis and interpretation of GWAS data. Prof Mulder is actively involved in training and education as well as curriculum development in bioinformatics and genomic medicine.
Deep X: Deep Learning with Deep Knowledge
Prof Volker Tresp
Labeled graphs can describe states and events at a cognitive abstraction level, representing facts as subject-predicate-object triples. A prominent and very successful example is the Google Knowledge Graph, representing on the order of 100B facts. Labeled graphs can be represented as adjacency tensors which can serve as inputs for prediction and decision making, and from which tensor models can be derived to generalize to unseen facts. We show how these ideas can be used, together with deep recurrent networks, for clinical decision support by predicting orders and outcomes. Following Goethe’s proverb, “you only see what you know”, we show how background knowledge can dramatically improve information extraction from images by deep convolutional networks and how tensor train models can be used for the efficient classification of videos. We discuss potential links to the memory and perceptual systems of the human brain. We conclude that tensor models, in connection with deep learning, can be the basis for many technical solutions requiring memory and perception, and might be a basis for modern AI.
Bio: Volker Tresp received a Diploma degree from the University of Goettingen, Germany, in 1984 and the M.Sc. and Ph.D. degrees from Yale University, New Haven, CT, in 1986 and 1989 respectively. Since 1989 he is the head of various research teams in machine learning at Siemens, Research and Technology. He filed more than 70 patent applications and was inventor of the year of Siemens in 1996. He has published more than 150 scientific articles and administered over 20 Ph.D. theses. The company Panoratio is a spin-off out of his team. His research focus in recent years has been „Machine Learning in Information Networks“ for modelling Knowledge Graphs, medical decision processes and sensor networks. He is the coordinator of one of the first nationally funded Big Data projects for the realization of „Precision Medicine“. Since 2011 he is also a Professor at the Ludwig Maximilian University of Munich where he teaches an annual course on Machine Learning.