Keynote Speakers

Madan Babu, PhD
Director of Center of Excellence for Data Driven Discovery,
Endowed Chair in Biological Data Science,
St. Jude Children's Research Hospital,
Memphis, TN., USA

Richard H.Scheuermann, PhD
J. Craig Venter Institute,
La Jolla, CA
and Department of Pathology,
University of California,
San Diego, CA

Li Shen, PhD
Professor and Interim Director of the Informatics Division,
Department of Biostatistics, Epidemiology and Informatics,
Perelman School of Medicine,
University of Pennsylvania ,
Philadelphia, PA



Abstract: Variation in GPCR signaling: Implications for drug discovery

Keywords: GPCR signaling; Drug Discovery; Data Science; Genetic Variation

Authors: M. Madan Babu, PhD, FRSC, FMedSci

G protein-coupled receptors (GPCRs) participate in diverse physiological processes, ranging from sensory responses such as vision, taste and smell to those regulating behavior, the immune and the cardiac system among others. The ~800 human GPCRs sense diverse signaling molecules such as hormones and neurotransmitters to allosterically activate the associated G proteins, which in turn regulate diverse intracellular signaling pathways. In this manner, GPCRs regulate virtually every aspect of human physiology. Not surprisingly, GPCRs are the targets of over one-third of all prescribed human drugs.

In this presentation, I will first discuss how one could leverage data from diverse species to infer selectivity determinants of GPCR-G protein binding, which is critical to elicit the right intracellular response. I will then discuss how one could utilize data on completely sequenced genomes of over 60,000 individuals from the human population to gain insights into natural receptor variation, which can result in variable drug response. Finally, I will present our recent work wherein by studying transcriptome data from over 30 different tissues in humans, one could begin to understand how alternative splicing creates diversity in GPCR signaling components, which may contribute to tissue-specific differences in receptor signaling. Such variations not only present challenges but also opportunities for drug development. I will conclude by discussing how understanding variation at these different dimensions, i.e., across different species, among different individuals of a species, and between tissues of a species, can provide a rich source of new hypotheses with implications for personalized medicine, drug development and understanding basic receptor biology.

M. Madan Babu is the Endowed Chair of Biological Data Science in the Department of Structural Biology at St Jude Children’s Research Hospital. He is the Director of the Center of Excellence for Data Driven Discovery. Before joining St Jude, Madan was a Programme Leader at the MRC Laboratory of Molecular Biology in Cambridge, UK (2006-2020). Madan’s research group develops data science approaches to make biological discoveries with a particular emphasis on understanding how the precise structure and intrinsically disordered regions of proteins contribute to cellular function. His interests and publications span diverse areas in life sciences relevant to human diseases and medicine. The work from his group has been recognized with national and international awards including the Royal Society’s Francis Crick Medal, and the Blavatnik Award for his work elucidating the functions of key proteins in the human genome. Recently, he was awarded the 2019 EMBO Gold Medal for his fundamental contributions to the field of computational molecular biology; specifically for his discoveries in the areas of G protein-coupled receptor (GPCR) signaling and intrinsically disordered proteins. Madan is the Chief Editor of Molecular Systems Biology and an executive editor of Nucleic Acids Research. He is an elected member of EMBO (2016), Fellow of the Royal Society of Chemistry (2017) and a Fellow of the UK Academy of Medical Science (2021). Madan did his post-doc from the NCBI, NIH (2006), received his Ph.D. from the MRC Lab of Molecular Biology and Trinity College, Cambridge University, UK (2004) and his undergraduate degree from the Centre for Biotechnology at Anna University in India (2001).

Abstract: Explainable artificial intelligence and single cell genomics to understand the cellular complexity of human brain

Elucidating the cellular architecture of the human cerebral cortex is central to understanding our cognitive abilities and susceptibility to disease. Using single-nucleus RNA-sequencing analysis to perform a comprehensive study of transcriptomic cell types in the human middle temporal gyrus and primary motor cortex, we have identified a highly diverse set of excitatory and inhibitory neuron types in healthy brain specimens (e.g., PMID: 31435019]. Using these data, we have developed an explainable machine learning method, NS-Forest [PMID: 34088715], to identify a minimum sets of marker genes that define each cell type identified and used these marker gene sets for the semantic representation of this knowledge in biomedical ontologies. NS-Forest marker gene identification also serves as a feature selection step that has been incorporated into a statistical graph-based framework using the FR-Match algorithm [PMID: 33249453] for comparing and matching transcriptomic data clusters for novel cell type discovery. The complete characterization of the cell type diversity of healthy human brain will serve as a foundation for understanding the cellular determinants of neurodegenerative, psychiatric, and other brain disorders.

Richard H.Scheuermann Ph.D., is the Director of Bioinformatics and La Jolla Campus Director of the J. Craig Venter Institute (JCVI). He is also an Adjunct Professor of Pathology at the University of California San Diego. He received a B.S. in Life Sciences from the Massachusetts Institute of Technology, and a Ph.D. in Molecular Biology from the University of California, Berkeley. Dr. Scheuermann has applied his deep knowledge of molecular immunology and infectious disease to develop novel computational data mining methods and knowledge representation approaches, including the development of biomedical ontologies and novel computational methods for gene expression, protein network, flow cytometry, and comparative genomics data analysis.  These informatics tools have been made available through public database and analysis resources, including the Immunology Database and Analysis Portal (ImmPort,, Influenza Research Database (IRD; and Virus Pathogen Resource (ViPR; More recently, Dr. Scheuermann has focused on the development of novel artificial intelligence approaches for interpreting single cell genomics data of the human immune and nervous systems.

Abstract: Brain imaging genetics: integrated analysis and machine learning

Brain imaging genetics is an emerging data science field, where integrated analysis of brain imaging and genetics data, often combined with other biomarker, clinical and environmental data, is performed to gain new insights into the genetic, molecular and phenotypic characteristics of the brain as well as their impact on normal and disordered brain function and behavior. Many methodological advances in brain imaging genetics are attributed to large-scale landmark biobank projects such as the Alzheimer’s Disease Sequencing Project, the Alzheimer’s Disease Neuroimaging Initiative, and the UK Biobank. Using the study of Alzheimer’s disease as an example, we will discuss fundamental concepts, state-of-the-art statistical and machine learning methods, and innovative applications in this rapidly evolving field. We show that the wide availability of brain imaging genetics data from various large-scale biobanks, coupled with advances in biomedical statistics, informatics and computing, provides enormous opportunities to contribute significantly to biomedical discoveries in brain science and to impact the development of new diagnostic, therapeutic and preventative approaches for complex brain disorders such as Alzheimer’s disease.

Li Shen is a Professor of Informatics and the Interim Director of the Informatics Division in the Department of Biostatistics, Epidemiology and Informatics at the Perelman School of Medicine in the University of Pennsylvania. He obtained his Ph.D. degree in Computer Science from Dartmouth College. His research interests include medical image computing, biomedical informatics, machine learning, network science, imaging genomics, multi-omics and systems biology, Alzheimer’s disease, and big data science in biomedicine. He has authored over 280 peer-reviewed articles in these fields. His work has been continuously supported by the NIH and NSF. His current research program is focused on developing computational and informatics methods for integrative analysis of multimodal imaging data, high throughput omics data, cognitive and other biomarker data, electronic health record (EHR) data, and rich biological knowledge such as pathways and networks, with applications to complex disorders such as Alzheimer’s disease. He has served on a variety of scientific journal editorial boards, grant review committees, and organizing committees of professional meetings in medical image computing and biomedical informatics. He served as the Executive Director of the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society between 2016 and 2019. He is a fellow of the American Institute for Medical and Biological Engineering.