Professor Hide graduated in Zoology with an Upper Second Honours from the University of Wales (Cardiff) in 1981. He attended graduate school at the Temple University, Philadelphia and graduated with a PhD in molecular genetics in 1991.
In 1992 he performed post doctoral training with Wen Hsuing-Li at the University of Texas, Houston, where he published his first Nature paper and in 1993 went on to train with David Pawson at the Smithsonian Institution National Natural History Museum, in 1994 with Richard Gibbs at the Baylor Human Genome Centre, Houston, and with Dan Davison at the University of Houston. In 1995 he gained industrial experience in Silicon Valley at MasPar Computer corporation as Director of Genomics.
In 1996 Professor Hide founded the South African National Bioinformatics Institute at the University of Western Cape, South Africa and was appointed Professor in 1999.
In 2007, he became visiting Professor of Bioinformatics at Harvard School of Public Health, and won the Oppenheimer Foundation Distinguished Sabbatical Research Fellowship.
In 2008, Hide was the subject of a directed search and became an associate professor at the Department of Biostatistics at Harvard School of Public Health. Also, in 2008 Hide founded the Harvard School of Public Health bioinformatics Core and became Director of the Harvard Stem Cell Institute Center for Stem Cell Bioinformatics.
In 2014, Hide accepted a Chair, and became Professor of Computational Biology, at the Sheffield Institute for Translational Neurosciences within the Department of Neuroscience at the University of Sheffield.
Hide has been awarded the National President’s Award for research in 1998, was elected to membership of the Academy of Science of South Africa in 2007 and also in 2007, won the Oppenheimer Foundation Distinguished Sabbatical Research Fellowship . In 2011, he was the first recipient of the International Society for Computational Biology award for Outstanding Achievement - in recognition of his work for the development of computational biology and bioinformatics in Africa.
Correlative approaches to interpretation of genetic contributions to disease
There is a need to move beyond the identification of individual genes associated with a disease. With the goal of using integrative genetic and functional models as a powerful approach to defining therapeutic targets for interventions; we have developed a series of functional maps for relationships between pathways. Current approaches for understanding directed interaction between pathways rely on shared genes, combining information from databases and interaction networks, or using direct physical interaction between genes and gene products to determine likely interaction.
We have extended this concept to define the relationship between pathways by their co-activity. Systematic quantification of the relationship between pathways provides a high-level map of related cellular functions to reveal the relationship between biological functions by their interactions. We interrogate disease variants in combination with disease gene expression signatures to reveal key interacting pathways enriched with disease variants. We extend genome variant associations in specific pathways enabling analysis of influence of previously unknown pathway relationships. A pathway correlation network (PCxN) reveals co-activity between gene sets for integrative genetic and functional models for experiment or genome association study.
As part of the Cure Alzheimer's Genome Project, we have discovered genomic loci that show both familial association with AD and genomic loci associated with disease resistance. These loci are in turn enriched with pathways and genes that undergo rewiring in direct association with plaque formation.
By applying this genetic data to a global functional interaction maps (Hide, Winston (2015): PCxN the Pathway co-activity Map https://dx.doi.org/10.6084/m9.figshare.00 AS subject, as1589792.v4) that reflect changes in the way cellular processes interact; we expose key functional dynamics of disease progression in close correlation with plaque density, neurofibrillary tangles in post-mortem brains, and degradation in cognitive function. Integrating these into comprehensive drug to target perturbation networks we predict those drugs that appear to most specifically interact to correct perturbations that result in disease.