I am a Research Fellow at the Department of Engineering and Big Data Institute. My work combines methods from Machine Learning, Bioinformatics, and Microscopy to understand the behaviour of biological systems and cancer evolution. In 2017, I was awarded a four-year Sir Henry Wellcome Research Fellowship to develop a knowledge-driven machine learning framework for characterising gene functions from high-throughput microscopy datasets. Before that, I did my PhD under the supervision of Professor Chris Bakal at the Institute of Cancer Research in London. I have a BSc in Computer Information Systems and a MSc in Data Warehousing and Data Mining.
I am passionate about outreach and inspiring young generation into STEM subjects. I participated in STEM ambassador scheme, science festivals, and In2Science initiative to host high school students from underprivileged backgrounds.
Research and Teaching
My research is focused on developing intelligent systems for facilitating biological discovery from large biomedical datasets including imaging and genomic datasets with a focus on Cancer. While at the ICR I 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, I discovered new links between cell shape and breast cancer progression. Recently, I developed an Artificial Intelligence technology for analysing large-scale genetic datasets toward automated discovery of gene functions.
I am also interested in data visualisation as an important tool for science communication. I 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.
I teach data visualisation, machine learning and software engineering at the graduate and undergraduate level at the Department of Engineering Science at the University of Oxford. I play an active role in supervising Masters and PhD students as well as tutoring.
Sailem H., Rittscher J., & Pelkmans L., (2020) KCML: a machine-learning framework for inference of multi-scale gene functions from genetic perturbation screens, Molecular Systems Biology
Sailem, H. & Bakal, C. (2017) Identification of clinically predictive metagenes that encode components of a network coupling cell shape to transcription by image-omics. Genome Research.
Sailem, H., Cooper, S. & Bakal C., (2016) Visualizing quantitative microscopy data: History and challenges, Critical reviews in biochemistry and molecular biology.
Sailem, H., Sero, J., & Bakal, C. (2015) Visualizing cellular imaging data using PhenoPlot. Nature Communications. (Highlighted in Nature Methods).
Sero, J.*, Sailem, H.*, Ardy, R., Almuttaqi, H., Zhang, T., & Bakal, C. (2015) Cell Shape and context regulate the nuclear translocation of NF-kappaB in breast epithelial and tumor Cells. Molecular Systems Biology.
*: The authors contributed equally
Sailem, H., Bousgouni, V., Cooper, S., & Bakal, C. (2014). Cross-talk between Rho and Rac GTPases drives deterministic exploration of cellular shape space and morphological heterogeneity. Open Biology.