SIB Bioinformatics Awards 2021 - Inferring gene functions from genetic perturbation screens
Channel: SIB - Swiss Institute of Bioinformatics
Date: 2021-10-07

What you will discover: a first of its kind methodology for the automated identification of tissue and context specific gene functions from experimental genetic screening data

Target audience: life scientists, bioinformaticians, image informaticians, image analysts and geneticists

Speaker: Heba Sailem, Research Fellow at the Institute of biomedical engineering at the University of Oxford

Reference paper
Sailem H Z et al. KCML: a machine-learning framework for inference of multi-scale gene functions from genetic perturbation screens, Molecular Systems Biology 2020, https://doi.org/10.15252/msb.20199083 

Any question about this talk? Contact Heba: heba.sailem[at]eng.ox.ac.uk @hsailem 

More about the speaker: 
Heba Sailem is a Sir Henry Wellcome Research Fellow at the Institute of biomedical engineering at the University of Oxford. Her research is focused on understanding the interplay between genetic and phenotypic components underlying changes in tissue architecture in cancer. To achieve that she develops machine learning, image analysis and statistical methodologies for analysing large microscopy and single cell datasets.
She pioneered the concept of knowledge-driven machine learning for inferring context-dependent gene functions from gene perturbation screens. She did her PhD at the Institute of Cancer Research in London. While at the ICR she developed methods for integrating phenotypic data with gene expression, modelling of the relationship between cell signalling and its context and modelling the dynamics of cell morphogenesis. In these studies, she discovered new links between cell shape and breast cancer progression. She is also interested in data visualisation as an important tool for science communication. She devised PhenoPlot, one of the first tools that are specifically designed for visualising phenotypic data. This method facilitates the interpretation of high dimensional data by generating pictorial representations of cells based on hundreds to thousands of measurements.


Read the news: https://www.sib.swiss/about-sib/news/10870-bioinformatics-awards-2021-a-series-of-talks-to-discover-the-laureates-work


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