Keynotes
Talk: Artificial Intelligence: history, benefits and risks, and some applications in bioinformatics
Distinguished Researcher Honoree
In this talk, I will initially present an historical perspective of how the area of artificial intelligence evolved, I will point out and present some examples of its benefits and risks and I will discuss some research projects that I participated, when I investigated the application of artificial Intelligence techniques to bioinformatics in the last 25 years.
Short Bio
ANDRÉ C. PONCE DE LEON F. DE CARVALHO
PhD in Electronic Engineering from the University of Kent, England, and Professor of Mathematical and Computer Sciences at the University of São Paulo. He researches Artificial Intelligence, Machine Learning, and Data Science, with applications in various fields. He was a three-time finalist for the Jabuti Award (winning in 2012 with the book “Artificial Intelligence: An Approach to Machine Learning”). He serves on international committees, including the UK Expert Advisory Panel for the AI Safety Report and AI Policy Research. He is the current coordinator of the Working Group on Machine Learning and Data Mining of the International Federation of Information Processing (IFIP). Is the coordinator of the Center of Applied Artificial Intelligence for Smart and Sustainable Cities, IARA and of the National Science and Technology Institute of Artificial Intelligence for Social Good IAPROBEM.
Talk: Drawing from phage diversity to uncover genomic factors shaping host interaction
Phages, viruses that infect bacteria, are highly diverse in natural ecosystems, but most are known only from genome sequences and their biology remains poorly understood. Using a large dataset of viral genomes derived from human gut metagenomes, we developed a framework to enable comparative analysis of phage genomes. This approach enabled the identification of genetic determinants of host range, providing insights into the mechanisms that drive phage-host interactions. These findings demonstrate how genomic diversity from natural environments can be leveraged to advance our understanding of uncultivated phages
Short Bio
ANTONIO PEDRO CAMARGO
Antonio Camargo is an assistant professor at the University of São Paulo (Brazil), where his primary research interest lies in the diversity and evolution of mobile genetic elements across Earth’s microbiomes and their evolutionary dynamics with host organisms. His current research focuses on developing computational methods that leverage large-scale sequencing data to generate insights into plasmid and phage biology.
Talk: Using Granger causality to explore the dynamic causality relations among genes associated with intellectual disability in human brain
Motivation: Intellectual disability (ID) is defined by an IQ under 70, in addition to deficits in two or more adaptive behaviors that affect everyday living. Throughout history, individuals with ID have often been marginalized from society and continue to suffer significantly even in modern times. A varying proportion of ID cases are attributable to genetic causes. Identifying the causal relation among these ID-associated genes and their gene expression pattern during brain development process would gain us a better understanding of the molecular basis of ID.
In this talk, I will focus on how to interpret gene expression data collected at different time points during the in vitro brain development process as time series and further introduce Granger causality test to evaluate the dynamic dependence relations among genes. These evaluations are used as input to construct gene expression network and extract the pathological information associated to ID including identifying new genes that can be critically related to the disease. To demonstrate these methods, I will demonstrate how to derive a priority list of new genes that are most likely associated with Mowat Wilson Syndrome via monitoring the community structure of ZEB2 in our Granger causality network constructed based on the Kutsche dataset (Kutsche, et al., 2018).
Short Bio
JING QUIN
Jing Qin’s main research areas are Mathematical Modelling and Extreme Value Statistics. Her research on mathematical modelling has a strong emphasis on interdisciplinary topics. Her research results have been published in top journals such as PNAS, Bioinformatics, BMC Bioinformatics, Journal of Computational Biology, Frontiers in Genetics, and Bulletin of Mathematical Biology. In addition, she has been serving as a senior editor for the Journal of the Royal Society Interface since 2017. Her research achievements on extreme value statistics focus on developing risk measures in multivariate extremes and their applications in both environmental and financial science. This line of research has led to publications in internationally leading journals in Statistics such as Extremes and Scandinavian Journal of Statistics.