Andre Altmann PhD, MRC Senior Research Fellow and proleptic Lecturer at the University College London (UCL).
Talk title: Combining genetics and imaging to better understand brain disorders
Abstract:In this talk I will speak about imaging genetics and its application to brain disorders, in particular Alzheimer’s disease and Epilepsy. In imaging genetics measures derived from (neuro-)imaging are used as endpoints in statistical analyses of genetic data. This is in contrast to conducting such studies with dichotomous case-control labels. Replacing crisp diagnostic labels with imaging marker that are thought to better reflect the disease process are expected to lend increased statistical power to such analyses and also allow to investigate different aspects of the underlying disease process.
Sara C. Madeira PhD, Associate Professor at Department of Informatics, Faculty of Sciences (FCUL), University of Lisbon.
Talk title: Biclustering and Triclustering Biomedical Data: Algorithms and Applications
Abstract: Three-dimensional data are increasingly prevalent across biomedical and social domains. Notable examples are gene-sample-time, individual-feature-time, or node-node-time data, generally referred to as observation-attribute-context data. The unsupervised analysis of three-dimensional data can be pursued to discover putative biological modules, disease progression profiles, and communities of individuals with coherent behavior, among other patterns of interest. It is thus key to enhance the understanding of complex biological, individual, and societal systems. In this context, although clustering can be applied to group observations, its relevance is limited since observations in three-dimensional data domains are typically only meaningfully correlated on subspaces of the overall space. Biclustering tacklesthis challenge but disregards the third dimension. In this scenario, triclustering, the discovery of coherent subspaces within three-dimensional data, has been largely researched to tackle these problems.
In this context, this talk focus biclustering and triclustering algorithms for biomedical data analysis. The goals are two-fold: 1) provide an overview on biclustering, the discovery of sets of objects with coherent values/patterns on subsets of features, shown to be key to unravel and characterize informative regions within tabular and network data in a variety of biomedical applications, where groups of genes/patients are only meaningfully related on subsets of the sampled/monitored conditions/time points; and 2) highlight challenges and opportunities to advance the field of triclustering and its applicability to complex three-dimensional data analysis.
Martin Hemberg PhD, Associate Group Leader at the Gurdon Institute and Career Development Fellow Group Leader at the Wellcome Sanger Institute.
Talk title: Towards predicting gene expression from sequence
Abstract: One of the central challenges in computational biology is to understand how a gene’s expression level is determined by regulatory sequences. Successful models have been presented for prokaryotes, but little progress has been made for complex eukaryotic genomes. Although bulk RNA-seq has been around for over a decade and demonstrated to provide accurate quantitative expression estimates, there are no reliable computational methods for de novo discovery of regulatory motifs. Two of the major obstacles to developing computational models are the limited number of replicates and the fact that bulk samples contain a mixture of cell types that may not share the same regulatory program. The advent of single-cell data could potentially overcome both of these issues. Each cell can be considered a replicate, and through clustering they can be assigned into homogenous groups. We have taken advantage of recent developments in machine learning to develop a convolutional neural network which makes it possible to simultaneously identify transcription factor binding sites and their contribution to gene expression levels. The method requires a set of ~100 genes with a shared regulatory programme, and we show that informative motifs can be discovered both for proximal promoters and distal enhancers.
Riccardo Bellazzi Full Professor of Bioengineering, University of Pavia, Italy
Tiziana Bonaldi Director at the Europen Insitute of Oncology
Carlo Combi Full Professor at the Department of Computer Science, University of Verona, Italy
Massimo Delledonne Full Professor at the Department of Biotechnology, University of Verona, Italy
Enrico Domenici President at the Microsoft Research - University of Trento Centre for Computational and Systems Biology, Italy
Aldo Scarpa Full Professor of Pathology, Department of Diagnostic and Public Healt, University of Verona, Italy
Giorgio Valle Professor of Molecular Biology at the Department of Biology, University of Padua, Italy